Overview

Brought to you by YData

Dataset statistics

Number of variables61
Number of observations2930
Missing cells7824
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory744.5 KiB
Average record size in memory260.2 B

Variable types

Numeric22
Categorical39

Alerts

1st_flr_sf is highly overall correlated with saleprice and 1 other fieldsHigh correlation
2nd_flr_sf is highly overall correlated with bedroom_abvgr and 2 other fieldsHigh correlation
bedroom_abvgr is highly overall correlated with 2nd_flr_sf and 2 other fieldsHigh correlation
bldg_type is highly overall correlated with kitchen_abvgr and 2 other fieldsHigh correlation
bsmt_cond is highly overall correlated with pool_qcHigh correlation
bsmt_unf_sf is highly overall correlated with bsmtfin_sf_1High correlation
bsmtfin_sf_1 is highly overall correlated with bsmt_unf_sfHigh correlation
central_air is highly overall correlated with pool_qcHigh correlation
electrical is highly overall correlated with pool_qcHigh correlation
exter_qual is highly overall correlated with kitchen_qual and 2 other fieldsHigh correlation
exterior_1st is highly overall correlated with exterior_2ndHigh correlation
exterior_2nd is highly overall correlated with exterior_1stHigh correlation
fence is highly overall correlated with utilitiesHigh correlation
foundation is highly overall correlated with year_builtHigh correlation
garage_area is highly overall correlated with garage_cars and 4 other fieldsHigh correlation
garage_cars is highly overall correlated with garage_area and 5 other fieldsHigh correlation
garage_yr_blt is highly overall correlated with garage_area and 5 other fieldsHigh correlation
gr_liv_area is highly overall correlated with 2nd_flr_sf and 5 other fieldsHigh correlation
half_bath is highly overall correlated with ms_subclass and 1 other fieldsHigh correlation
house_style is highly overall correlated with ms_subclassHigh correlation
kitchen_abvgr is highly overall correlated with bldg_type and 1 other fieldsHigh correlation
kitchen_qual is highly overall correlated with exter_qualHigh correlation
ms_subclass is highly overall correlated with bldg_type and 2 other fieldsHigh correlation
ms_zoning is highly overall correlated with neighborhood and 1 other fieldsHigh correlation
neighborhood is highly overall correlated with ms_zoning and 1 other fieldsHigh correlation
overall_qual is highly overall correlated with exter_qual and 7 other fieldsHigh correlation
pid is highly overall correlated with neighborhoodHigh correlation
pool_qc is highly overall correlated with bldg_type and 8 other fieldsHigh correlation
saleprice is highly overall correlated with 1st_flr_sf and 9 other fieldsHigh correlation
street is highly overall correlated with pool_qcHigh correlation
total_bsmt_sf is highly overall correlated with 1st_flr_sf and 1 other fieldsHigh correlation
totrms_abvgrd is highly overall correlated with 2nd_flr_sf and 2 other fieldsHigh correlation
utilities is highly overall correlated with fence and 1 other fieldsHigh correlation
year_built is highly overall correlated with foundation and 6 other fieldsHigh correlation
year_remod_add is highly overall correlated with garage_yr_blt and 3 other fieldsHigh correlation
ms_zoning is highly imbalanced (62.7%) Imbalance
street is highly imbalanced (96.2%) Imbalance
land_contour is highly imbalanced (68.6%) Imbalance
utilities is highly imbalanced (99.2%) Imbalance
condition_1 is highly imbalanced (71.1%) Imbalance
bldg_type is highly imbalanced (57.9%) Imbalance
roof_style is highly imbalanced (66.4%) Imbalance
exter_cond is highly imbalanced (70.8%) Imbalance
bsmt_cond is highly imbalanced (78.1%) Imbalance
central_air is highly imbalanced (64.6%) Imbalance
electrical is highly imbalanced (78.5%) Imbalance
bsmt_half_bath is highly imbalanced (78.8%) Imbalance
kitchen_abvgr is highly imbalanced (86.0%) Imbalance
functional is highly imbalanced (83.0%) Imbalance
garage_qual is highly imbalanced (84.2%) Imbalance
garage_cond is highly imbalanced (87.8%) Imbalance
paved_drive is highly imbalanced (66.9%) Imbalance
bsmt_qual has 80 (2.7%) missing values Missing
bsmt_cond has 80 (2.7%) missing values Missing
bsmt_exposure has 83 (2.8%) missing values Missing
bsmtfin_type_1 has 80 (2.7%) missing values Missing
fireplace_qu has 1422 (48.5%) missing values Missing
garage_type has 157 (5.4%) missing values Missing
garage_yr_blt has 159 (5.4%) missing values Missing
garage_finish has 159 (5.4%) missing values Missing
garage_qual has 159 (5.4%) missing values Missing
garage_cond has 159 (5.4%) missing values Missing
pool_qc has 2917 (99.6%) missing values Missing
fence has 2358 (80.5%) missing values Missing
pid has unique values Unique
bsmtfin_sf_1 has 930 (31.7%) zeros Zeros
bsmtfin_sf_2 has 2578 (88.0%) zeros Zeros
bsmt_unf_sf has 244 (8.3%) zeros Zeros
total_bsmt_sf has 79 (2.7%) zeros Zeros
2nd_flr_sf has 1678 (57.3%) zeros Zeros
garage_cars has 157 (5.4%) zeros Zeros
garage_area has 157 (5.4%) zeros Zeros
wood_deck_sf has 1526 (52.1%) zeros Zeros
open_porch_sf has 1300 (44.4%) zeros Zeros

Reproduction

Analysis started2025-01-08 16:52:36.862380
Analysis finished2025-01-08 16:54:15.187441
Duration1 minute and 38.33 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

pid
Real number (ℝ)

High correlation  Unique 

Distinct2930
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.144645 × 108
Minimum5.263011 × 108
Maximum1.0071001 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:15.406524image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5.263011 × 108
5-th percentile5.273012 × 108
Q15.2847702 × 108
median5.3545362 × 108
Q39.071811 × 108
95-th percentile9.1640324 × 108
Maximum1.0071001 × 109
Range4.8079901 × 108
Interquartile range (IQR)3.7870408 × 108

Descriptive statistics

Standard deviation1.8873084 × 108
Coefficient of variation (CV)0.26415707
Kurtosis-1.9951464
Mean7.144645 × 108
Median Absolute Deviation (MAD)8345605
Skewness0.05588589
Sum2.093381 × 1012
Variance3.5619332 × 1016
MonotonicityNot monotonic
2025-01-08T16:54:15.660653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
924151050 1
 
< 0.1%
526301100 1
 
< 0.1%
923226180 1
 
< 0.1%
923226150 1
 
< 0.1%
923225510 1
 
< 0.1%
923225260 1
 
< 0.1%
923225240 1
 
< 0.1%
923225190 1
 
< 0.1%
923205120 1
 
< 0.1%
923203100 1
 
< 0.1%
Other values (2920) 2920
99.7%
ValueCountFrequency (%)
526301100 1
< 0.1%
526302030 1
< 0.1%
526302040 1
< 0.1%
526302110 1
< 0.1%
526302120 1
< 0.1%
526303060 1
< 0.1%
526350040 1
< 0.1%
526351010 1
< 0.1%
526351030 1
< 0.1%
526351100 1
< 0.1%
ValueCountFrequency (%)
1007100110 1
< 0.1%
924152030 1
< 0.1%
924151050 1
< 0.1%
924151040 1
< 0.1%
924100070 1
< 0.1%
924100060 1
< 0.1%
924100050 1
< 0.1%
924100040 1
< 0.1%
924100020 1
< 0.1%
923426070 1
< 0.1%

ms_subclass
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.387372
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:15.827623image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.638025
Coefficient of variation (CV)0.74298618
Kurtosis1.386775
Mean57.387372
Median Absolute Deviation (MAD)30
Skewness1.3575794
Sum168145
Variance1818.0011
MonotonicityNot monotonic
2025-01-08T16:54:15.969551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20 1079
36.8%
60 575
19.6%
50 287
 
9.8%
120 192
 
6.6%
30 139
 
4.7%
160 129
 
4.4%
70 128
 
4.4%
80 118
 
4.0%
90 109
 
3.7%
190 61
 
2.1%
Other values (6) 113
 
3.9%
ValueCountFrequency (%)
20 1079
36.8%
30 139
 
4.7%
40 6
 
0.2%
45 18
 
0.6%
50 287
 
9.8%
60 575
19.6%
70 128
 
4.4%
75 23
 
0.8%
80 118
 
4.0%
85 48
 
1.6%
ValueCountFrequency (%)
190 61
 
2.1%
180 17
 
0.6%
160 129
4.4%
150 1
 
< 0.1%
120 192
6.6%
90 109
3.7%
85 48
 
1.6%
80 118
4.0%
75 23
 
0.8%
70 128
4.4%

ms_zoning
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
RL
2273 
RM
462 
FV
 
139
RH
 
27
C
 
25
Other values (2)
 
4

Length

Max length2
Median length2
Mean length1.9901024
Min length1

Characters and Unicode

Total characters5831
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRH
3rd rowRL
4th rowRL
5th rowRL

Common Values

ValueCountFrequency (%)
RL 2273
77.6%
RM 462
 
15.8%
FV 139
 
4.7%
RH 27
 
0.9%
C 25
 
0.9%
A 2
 
0.1%
I 2
 
0.1%

Length

2025-01-08T16:54:16.130516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:16.454356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
rl 2273
77.6%
rm 462
 
15.8%
fv 139
 
4.7%
rh 27
 
0.9%
c 25
 
0.9%
a 2
 
0.1%
i 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
R 2762
47.4%
L 2273
39.0%
M 462
 
7.9%
F 139
 
2.4%
V 139
 
2.4%
H 27
 
0.5%
C 25
 
0.4%
A 2
 
< 0.1%
I 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5831
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 2762
47.4%
L 2273
39.0%
M 462
 
7.9%
F 139
 
2.4%
V 139
 
2.4%
H 27
 
0.5%
C 25
 
0.4%
A 2
 
< 0.1%
I 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5831
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 2762
47.4%
L 2273
39.0%
M 462
 
7.9%
F 139
 
2.4%
V 139
 
2.4%
H 27
 
0.5%
C 25
 
0.4%
A 2
 
< 0.1%
I 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5831
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 2762
47.4%
L 2273
39.0%
M 462
 
7.9%
F 139
 
2.4%
V 139
 
2.4%
H 27
 
0.5%
C 25
 
0.4%
A 2
 
< 0.1%
I 2
 
< 0.1%

lot_area
Real number (ℝ)

Distinct1960
Distinct (%)66.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10147.922
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:16.674412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3188.3
Q17440.25
median9436.5
Q311555.25
95-th percentile17131
Maximum215245
Range213945
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation7880.0178
Coefficient of variation (CV)0.77651542
Kurtosis265.02367
Mean10147.922
Median Absolute Deviation (MAD)2040
Skewness12.820898
Sum29733411
Variance62094680
MonotonicityNot monotonic
2025-01-08T16:54:16.954321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9600 44
 
1.5%
7200 43
 
1.5%
6000 34
 
1.2%
9000 29
 
1.0%
10800 25
 
0.9%
8400 21
 
0.7%
7500 21
 
0.7%
6240 18
 
0.6%
1680 18
 
0.6%
6120 17
 
0.6%
Other values (1950) 2660
90.8%
ValueCountFrequency (%)
1300 1
< 0.1%
1470 1
< 0.1%
1476 1
< 0.1%
1477 2
0.1%
1484 1
< 0.1%
1488 1
< 0.1%
1491 1
< 0.1%
1495 1
< 0.1%
1504 1
< 0.1%
1526 2
0.1%
ValueCountFrequency (%)
215245 1
< 0.1%
164660 1
< 0.1%
159000 1
< 0.1%
115149 1
< 0.1%
70761 1
< 0.1%
63887 1
< 0.1%
57200 1
< 0.1%
56600 1
< 0.1%
53504 1
< 0.1%
53227 1
< 0.1%

street
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Pave
2918 
Grvl
 
12

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters11720
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave

Common Values

ValueCountFrequency (%)
Pave 2918
99.6%
Grvl 12
 
0.4%

Length

2025-01-08T16:54:17.160543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:17.328323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
pave 2918
99.6%
grvl 12
 
0.4%

Most occurring characters

ValueCountFrequency (%)
v 2930
25.0%
P 2918
24.9%
a 2918
24.9%
e 2918
24.9%
G 12
 
0.1%
r 12
 
0.1%
l 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v 2930
25.0%
P 2918
24.9%
a 2918
24.9%
e 2918
24.9%
G 12
 
0.1%
r 12
 
0.1%
l 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v 2930
25.0%
P 2918
24.9%
a 2918
24.9%
e 2918
24.9%
G 12
 
0.1%
r 12
 
0.1%
l 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v 2930
25.0%
P 2918
24.9%
a 2918
24.9%
e 2918
24.9%
G 12
 
0.1%
r 12
 
0.1%
l 12
 
0.1%

lot_shape
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Reg
1859 
IR1
979 
IR2
 
76
IR3
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8790
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIR1
2nd rowReg
3rd rowIR1
4th rowReg
5th rowIR1

Common Values

ValueCountFrequency (%)
Reg 1859
63.4%
IR1 979
33.4%
IR2 76
 
2.6%
IR3 16
 
0.5%

Length

2025-01-08T16:54:17.548952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:17.696492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
reg 1859
63.4%
ir1 979
33.4%
ir2 76
 
2.6%
ir3 16
 
0.5%

Most occurring characters

ValueCountFrequency (%)
R 2930
33.3%
e 1859
21.1%
g 1859
21.1%
I 1071
 
12.2%
1 979
 
11.1%
2 76
 
0.9%
3 16
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8790
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 2930
33.3%
e 1859
21.1%
g 1859
21.1%
I 1071
 
12.2%
1 979
 
11.1%
2 76
 
0.9%
3 16
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8790
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 2930
33.3%
e 1859
21.1%
g 1859
21.1%
I 1071
 
12.2%
1 979
 
11.1%
2 76
 
0.9%
3 16
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8790
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 2930
33.3%
e 1859
21.1%
g 1859
21.1%
I 1071
 
12.2%
1 979
 
11.1%
2 76
 
0.9%
3 16
 
0.2%

land_contour
Categorical

Imbalance 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Lvl
2633 
HLS
 
120
Bnk
 
117
Low
 
60

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8790
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl

Common Values

ValueCountFrequency (%)
Lvl 2633
89.9%
HLS 120
 
4.1%
Bnk 117
 
4.0%
Low 60
 
2.0%

Length

2025-01-08T16:54:17.918769image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:18.068899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
lvl 2633
89.9%
hls 120
 
4.1%
bnk 117
 
4.0%
low 60
 
2.0%

Most occurring characters

ValueCountFrequency (%)
L 2813
32.0%
v 2633
30.0%
l 2633
30.0%
H 120
 
1.4%
S 120
 
1.4%
B 117
 
1.3%
n 117
 
1.3%
k 117
 
1.3%
o 60
 
0.7%
w 60
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8790
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 2813
32.0%
v 2633
30.0%
l 2633
30.0%
H 120
 
1.4%
S 120
 
1.4%
B 117
 
1.3%
n 117
 
1.3%
k 117
 
1.3%
o 60
 
0.7%
w 60
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8790
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 2813
32.0%
v 2633
30.0%
l 2633
30.0%
H 120
 
1.4%
S 120
 
1.4%
B 117
 
1.3%
n 117
 
1.3%
k 117
 
1.3%
o 60
 
0.7%
w 60
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8790
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 2813
32.0%
v 2633
30.0%
l 2633
30.0%
H 120
 
1.4%
S 120
 
1.4%
B 117
 
1.3%
n 117
 
1.3%
k 117
 
1.3%
o 60
 
0.7%
w 60
 
0.7%

utilities
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
AllPub
2927 
NoSewr
 
2
NoSeWa
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters17580
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub

Common Values

ValueCountFrequency (%)
AllPub 2927
99.9%
NoSewr 2
 
0.1%
NoSeWa 1
 
< 0.1%

Length

2025-01-08T16:54:18.318890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:18.551377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
allpub 2927
99.9%
nosewr 2
 
0.1%
nosewa 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l 5854
33.3%
A 2927
16.6%
P 2927
16.6%
u 2927
16.6%
b 2927
16.6%
N 3
 
< 0.1%
o 3
 
< 0.1%
S 3
 
< 0.1%
e 3
 
< 0.1%
w 2
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17580
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 5854
33.3%
A 2927
16.6%
P 2927
16.6%
u 2927
16.6%
b 2927
16.6%
N 3
 
< 0.1%
o 3
 
< 0.1%
S 3
 
< 0.1%
e 3
 
< 0.1%
w 2
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17580
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 5854
33.3%
A 2927
16.6%
P 2927
16.6%
u 2927
16.6%
b 2927
16.6%
N 3
 
< 0.1%
o 3
 
< 0.1%
S 3
 
< 0.1%
e 3
 
< 0.1%
w 2
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17580
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 5854
33.3%
A 2927
16.6%
P 2927
16.6%
u 2927
16.6%
b 2927
16.6%
N 3
 
< 0.1%
o 3
 
< 0.1%
S 3
 
< 0.1%
e 3
 
< 0.1%
w 2
 
< 0.1%
Other values (3) 4
 
< 0.1%

lot_config
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Inside
2140 
Corner
511 
CulDSac
 
180
FR2
 
85
FR3
 
14

Length

Max length7
Median length6
Mean length5.9600683
Min length3

Characters and Unicode

Total characters17463
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorner
2nd rowInside
3rd rowCorner
4th rowCorner
5th rowInside

Common Values

ValueCountFrequency (%)
Inside 2140
73.0%
Corner 511
 
17.4%
CulDSac 180
 
6.1%
FR2 85
 
2.9%
FR3 14
 
0.5%

Length

2025-01-08T16:54:18.722469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:18.908987image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
inside 2140
73.0%
corner 511
 
17.4%
culdsac 180
 
6.1%
fr2 85
 
2.9%
fr3 14
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 2651
15.2%
e 2651
15.2%
I 2140
12.3%
s 2140
12.3%
i 2140
12.3%
d 2140
12.3%
r 1022
 
5.9%
C 691
 
4.0%
o 511
 
2.9%
u 180
 
1.0%
Other values (9) 1197
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17463
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2651
15.2%
e 2651
15.2%
I 2140
12.3%
s 2140
12.3%
i 2140
12.3%
d 2140
12.3%
r 1022
 
5.9%
C 691
 
4.0%
o 511
 
2.9%
u 180
 
1.0%
Other values (9) 1197
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17463
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2651
15.2%
e 2651
15.2%
I 2140
12.3%
s 2140
12.3%
i 2140
12.3%
d 2140
12.3%
r 1022
 
5.9%
C 691
 
4.0%
o 511
 
2.9%
u 180
 
1.0%
Other values (9) 1197
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17463
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2651
15.2%
e 2651
15.2%
I 2140
12.3%
s 2140
12.3%
i 2140
12.3%
d 2140
12.3%
r 1022
 
5.9%
C 691
 
4.0%
o 511
 
2.9%
u 180
 
1.0%
Other values (9) 1197
6.9%

neighborhood
Categorical

High correlation 

Distinct28
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
NAmes
443 
CollgCr
267 
OldTown
239 
Edwards
194 
Somerst
182 
Other values (23)
1605 

Length

Max length7
Median length7
Mean length6.4993174
Min length5

Characters and Unicode

Total characters19043
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNAmes
2nd rowNAmes
3rd rowNAmes
4th rowNAmes
5th rowGilbert

Common Values

ValueCountFrequency (%)
NAmes 443
15.1%
CollgCr 267
 
9.1%
OldTown 239
 
8.2%
Edwards 194
 
6.6%
Somerst 182
 
6.2%
NridgHt 166
 
5.7%
Gilbert 165
 
5.6%
Sawyer 151
 
5.2%
NWAmes 131
 
4.5%
SawyerW 125
 
4.3%
Other values (18) 867
29.6%

Length

2025-01-08T16:54:19.086317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
names 443
15.1%
collgcr 267
 
9.1%
oldtown 239
 
8.2%
edwards 194
 
6.6%
somerst 182
 
6.2%
nridght 166
 
5.7%
gilbert 165
 
5.6%
sawyer 151
 
5.2%
nwames 131
 
4.5%
sawyerw 125
 
4.3%
Other values (18) 867
29.6%

Most occurring characters

ValueCountFrequency (%)
r 1840
 
9.7%
e 1832
 
9.6%
l 1214
 
6.4%
d 1010
 
5.3%
s 968
 
5.1%
o 950
 
5.0%
m 857
 
4.5%
w 849
 
4.5%
N 834
 
4.4%
C 725
 
3.8%
Other values (29) 7964
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1840
 
9.7%
e 1832
 
9.6%
l 1214
 
6.4%
d 1010
 
5.3%
s 968
 
5.1%
o 950
 
5.0%
m 857
 
4.5%
w 849
 
4.5%
N 834
 
4.4%
C 725
 
3.8%
Other values (29) 7964
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1840
 
9.7%
e 1832
 
9.6%
l 1214
 
6.4%
d 1010
 
5.3%
s 968
 
5.1%
o 950
 
5.0%
m 857
 
4.5%
w 849
 
4.5%
N 834
 
4.4%
C 725
 
3.8%
Other values (29) 7964
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1840
 
9.7%
e 1832
 
9.6%
l 1214
 
6.4%
d 1010
 
5.3%
s 968
 
5.1%
o 950
 
5.0%
m 857
 
4.5%
w 849
 
4.5%
N 834
 
4.4%
C 725
 
3.8%
Other values (29) 7964
41.8%

condition_1
Categorical

Imbalance 

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
Norm
2522 
Feedr
 
164
Artery
 
92
RRAn
 
50
PosN
 
39
Other values (4)
 
63

Length

Max length6
Median length4
Mean length4.1187713
Min length4

Characters and Unicode

Total characters12068
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowFeedr
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 2522
86.1%
Feedr 164
 
5.6%
Artery 92
 
3.1%
RRAn 50
 
1.7%
PosN 39
 
1.3%
RRAe 28
 
1.0%
PosA 20
 
0.7%
RRNn 9
 
0.3%
RRNe 6
 
0.2%

Length

2025-01-08T16:54:19.281964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:19.503638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
norm 2522
86.1%
feedr 164
 
5.6%
artery 92
 
3.1%
rran 50
 
1.7%
posn 39
 
1.3%
rrae 28
 
1.0%
posa 20
 
0.7%
rrnn 9
 
0.3%
rrne 6
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 2870
23.8%
o 2581
21.4%
N 2576
21.3%
m 2522
20.9%
e 454
 
3.8%
A 190
 
1.6%
R 186
 
1.5%
F 164
 
1.4%
d 164
 
1.4%
t 92
 
0.8%
Other values (4) 269
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2870
23.8%
o 2581
21.4%
N 2576
21.3%
m 2522
20.9%
e 454
 
3.8%
A 190
 
1.6%
R 186
 
1.5%
F 164
 
1.4%
d 164
 
1.4%
t 92
 
0.8%
Other values (4) 269
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2870
23.8%
o 2581
21.4%
N 2576
21.3%
m 2522
20.9%
e 454
 
3.8%
A 190
 
1.6%
R 186
 
1.5%
F 164
 
1.4%
d 164
 
1.4%
t 92
 
0.8%
Other values (4) 269
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2870
23.8%
o 2581
21.4%
N 2576
21.3%
m 2522
20.9%
e 454
 
3.8%
A 190
 
1.6%
R 186
 
1.5%
F 164
 
1.4%
d 164
 
1.4%
t 92
 
0.8%
Other values (4) 269
 
2.2%

bldg_type
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
1Fam
2425 
TwnhsE
 
233
Duplex
 
109
Twnhs
 
101
2fmCon
 
62

Length

Max length6
Median length4
Mean length4.3102389
Min length4

Characters and Unicode

Total characters12629
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam

Common Values

ValueCountFrequency (%)
1Fam 2425
82.8%
TwnhsE 233
 
8.0%
Duplex 109
 
3.7%
Twnhs 101
 
3.4%
2fmCon 62
 
2.1%

Length

2025-01-08T16:54:19.692053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:19.857922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1fam 2425
82.8%
twnhse 233
 
8.0%
duplex 109
 
3.7%
twnhs 101
 
3.4%
2fmcon 62
 
2.1%

Most occurring characters

ValueCountFrequency (%)
m 2487
19.7%
1 2425
19.2%
F 2425
19.2%
a 2425
19.2%
n 396
 
3.1%
T 334
 
2.6%
w 334
 
2.6%
h 334
 
2.6%
s 334
 
2.6%
E 233
 
1.8%
Other values (10) 902
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12629
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 2487
19.7%
1 2425
19.2%
F 2425
19.2%
a 2425
19.2%
n 396
 
3.1%
T 334
 
2.6%
w 334
 
2.6%
h 334
 
2.6%
s 334
 
2.6%
E 233
 
1.8%
Other values (10) 902
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12629
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 2487
19.7%
1 2425
19.2%
F 2425
19.2%
a 2425
19.2%
n 396
 
3.1%
T 334
 
2.6%
w 334
 
2.6%
h 334
 
2.6%
s 334
 
2.6%
E 233
 
1.8%
Other values (10) 902
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12629
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 2487
19.7%
1 2425
19.2%
F 2425
19.2%
a 2425
19.2%
n 396
 
3.1%
T 334
 
2.6%
w 334
 
2.6%
h 334
 
2.6%
s 334
 
2.6%
E 233
 
1.8%
Other values (10) 902
 
7.1%

house_style
Categorical

High correlation 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
1Story
1481 
2Story
873 
1.5Fin
314 
SLvl
 
128
SFoyer
 
83
Other values (3)
 
51

Length

Max length6
Median length6
Mean length5.912628
Min length4

Characters and Unicode

Total characters17324
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Story
2nd row1Story
3rd row1Story
4th row1Story
5th row2Story

Common Values

ValueCountFrequency (%)
1Story 1481
50.5%
2Story 873
29.8%
1.5Fin 314
 
10.7%
SLvl 128
 
4.4%
SFoyer 83
 
2.8%
2.5Unf 24
 
0.8%
1.5Unf 19
 
0.6%
2.5Fin 8
 
0.3%

Length

2025-01-08T16:54:20.038333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:20.219623image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1story 1481
50.5%
2story 873
29.8%
1.5fin 314
 
10.7%
slvl 128
 
4.4%
sfoyer 83
 
2.8%
2.5unf 24
 
0.8%
1.5unf 19
 
0.6%
2.5fin 8
 
0.3%

Most occurring characters

ValueCountFrequency (%)
S 2565
14.8%
o 2437
14.1%
y 2437
14.1%
r 2437
14.1%
t 2354
13.6%
1 1814
10.5%
2 905
 
5.2%
F 405
 
2.3%
. 365
 
2.1%
5 365
 
2.1%
Other values (8) 1240
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17324
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2565
14.8%
o 2437
14.1%
y 2437
14.1%
r 2437
14.1%
t 2354
13.6%
1 1814
10.5%
2 905
 
5.2%
F 405
 
2.3%
. 365
 
2.1%
5 365
 
2.1%
Other values (8) 1240
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17324
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2565
14.8%
o 2437
14.1%
y 2437
14.1%
r 2437
14.1%
t 2354
13.6%
1 1814
10.5%
2 905
 
5.2%
F 405
 
2.3%
. 365
 
2.1%
5 365
 
2.1%
Other values (8) 1240
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17324
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2565
14.8%
o 2437
14.1%
y 2437
14.1%
r 2437
14.1%
t 2354
13.6%
1 1814
10.5%
2 905
 
5.2%
F 405
 
2.3%
. 365
 
2.1%
5 365
 
2.1%
Other values (8) 1240
7.2%

overall_qual
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0948805
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:20.433983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4110261
Coefficient of variation (CV)0.23151005
Kurtosis0.05241245
Mean6.0948805
Median Absolute Deviation (MAD)1
Skewness0.19063396
Sum17858
Variance1.9909946
MonotonicityNot monotonic
2025-01-08T16:54:20.604810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 825
28.2%
6 732
25.0%
7 602
20.5%
8 350
11.9%
4 226
 
7.7%
9 107
 
3.7%
3 40
 
1.4%
10 31
 
1.1%
2 13
 
0.4%
1 4
 
0.1%
ValueCountFrequency (%)
1 4
 
0.1%
2 13
 
0.4%
3 40
 
1.4%
4 226
 
7.7%
5 825
28.2%
6 732
25.0%
7 602
20.5%
8 350
11.9%
9 107
 
3.7%
10 31
 
1.1%
ValueCountFrequency (%)
10 31
 
1.1%
9 107
 
3.7%
8 350
11.9%
7 602
20.5%
6 732
25.0%
5 825
28.2%
4 226
 
7.7%
3 40
 
1.4%
2 13
 
0.4%
1 4
 
0.1%

overall_cond
Real number (ℝ)

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5631399
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:20.770391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1115366
Coefficient of variation (CV)0.19980381
Kurtosis1.4914497
Mean5.5631399
Median Absolute Deviation (MAD)0
Skewness0.57442948
Sum16300
Variance1.2355135
MonotonicityNot monotonic
2025-01-08T16:54:20.928599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 1654
56.5%
6 533
 
18.2%
7 390
 
13.3%
8 144
 
4.9%
4 101
 
3.4%
3 50
 
1.7%
9 41
 
1.4%
2 10
 
0.3%
1 7
 
0.2%
ValueCountFrequency (%)
1 7
 
0.2%
2 10
 
0.3%
3 50
 
1.7%
4 101
 
3.4%
5 1654
56.5%
6 533
 
18.2%
7 390
 
13.3%
8 144
 
4.9%
9 41
 
1.4%
ValueCountFrequency (%)
9 41
 
1.4%
8 144
 
4.9%
7 390
 
13.3%
6 533
 
18.2%
5 1654
56.5%
4 101
 
3.4%
3 50
 
1.7%
2 10
 
0.3%
1 7
 
0.2%

year_built
Real number (ℝ)

High correlation 

Distinct118
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.3563
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:21.138305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1915
Q11954
median1973
Q32001
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)47

Descriptive statistics

Standard deviation30.245361
Coefficient of variation (CV)0.015342412
Kurtosis-0.50171504
Mean1971.3563
Median Absolute Deviation (MAD)25
Skewness-0.60446222
Sum5776074
Variance914.78184
MonotonicityNot monotonic
2025-01-08T16:54:21.394484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 142
 
4.8%
2006 138
 
4.7%
2007 109
 
3.7%
2004 99
 
3.4%
2003 88
 
3.0%
1977 57
 
1.9%
1920 57
 
1.9%
1976 54
 
1.8%
1999 52
 
1.8%
2008 49
 
1.7%
Other values (108) 2085
71.2%
ValueCountFrequency (%)
1872 1
 
< 0.1%
1875 1
 
< 0.1%
1879 1
 
< 0.1%
1880 5
0.2%
1882 1
 
< 0.1%
1885 2
 
0.1%
1890 7
0.2%
1892 2
 
0.1%
1893 1
 
< 0.1%
1895 3
0.1%
ValueCountFrequency (%)
2010 3
 
0.1%
2009 25
 
0.9%
2008 49
 
1.7%
2007 109
3.7%
2006 138
4.7%
2005 142
4.8%
2004 99
3.4%
2003 88
3.0%
2002 47
 
1.6%
2001 35
 
1.2%

year_remod_add
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.2666
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:21.587514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11965
median1993
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)39

Descriptive statistics

Standard deviation20.860286
Coefficient of variation (CV)0.010512845
Kurtosis-1.3415159
Mean1984.2666
Median Absolute Deviation (MAD)14
Skewness-0.45186265
Sum5813901
Variance435.15153
MonotonicityNot monotonic
2025-01-08T16:54:21.792585image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 361
 
12.3%
2006 202
 
6.9%
2007 164
 
5.6%
2005 141
 
4.8%
2004 111
 
3.8%
2000 104
 
3.5%
2003 99
 
3.4%
2002 82
 
2.8%
2008 81
 
2.8%
1998 79
 
2.7%
Other values (51) 1506
51.4%
ValueCountFrequency (%)
1950 361
12.3%
1951 14
 
0.5%
1952 15
 
0.5%
1953 20
 
0.7%
1954 28
 
1.0%
1955 25
 
0.9%
1956 30
 
1.0%
1957 20
 
0.7%
1958 34
 
1.2%
1959 30
 
1.0%
ValueCountFrequency (%)
2010 13
 
0.4%
2009 34
 
1.2%
2008 81
2.8%
2007 164
5.6%
2006 202
6.9%
2005 141
4.8%
2004 111
3.8%
2003 99
3.4%
2002 82
2.8%
2001 49
 
1.7%

roof_style
Categorical

Imbalance 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Gable
2321 
Hip
551 
Gambrel
 
22
Flat
 
20
Mansard
 
11

Length

Max length7
Median length5
Mean length4.637884
Min length3

Characters and Unicode

Total characters13589
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHip
2nd rowGable
3rd rowHip
4th rowHip
5th rowGable

Common Values

ValueCountFrequency (%)
Gable 2321
79.2%
Hip 551
 
18.8%
Gambrel 22
 
0.8%
Flat 20
 
0.7%
Mansard 11
 
0.4%
Shed 5
 
0.2%

Length

2025-01-08T16:54:22.015983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:22.211269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
gable 2321
79.2%
hip 551
 
18.8%
gambrel 22
 
0.8%
flat 20
 
0.7%
mansard 11
 
0.4%
shed 5
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 2385
17.6%
l 2363
17.4%
e 2348
17.3%
G 2343
17.2%
b 2343
17.2%
H 551
 
4.1%
i 551
 
4.1%
p 551
 
4.1%
r 33
 
0.2%
m 22
 
0.2%
Other values (8) 99
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13589
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2385
17.6%
l 2363
17.4%
e 2348
17.3%
G 2343
17.2%
b 2343
17.2%
H 551
 
4.1%
i 551
 
4.1%
p 551
 
4.1%
r 33
 
0.2%
m 22
 
0.2%
Other values (8) 99
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13589
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2385
17.6%
l 2363
17.4%
e 2348
17.3%
G 2343
17.2%
b 2343
17.2%
H 551
 
4.1%
i 551
 
4.1%
p 551
 
4.1%
r 33
 
0.2%
m 22
 
0.2%
Other values (8) 99
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13589
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2385
17.6%
l 2363
17.4%
e 2348
17.3%
G 2343
17.2%
b 2343
17.2%
H 551
 
4.1%
i 551
 
4.1%
p 551
 
4.1%
r 33
 
0.2%
m 22
 
0.2%
Other values (8) 99
 
0.7%

exterior_1st
Categorical

High correlation 

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
VinylSd
1026 
MetalSd
450 
HdBoard
442 
Wd Sdng
420 
Plywood
221 
Other values (11)
371 

Length

Max length7
Median length7
Mean length6.9832765
Min length5

Characters and Unicode

Total characters20461
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowBrkFace
2nd rowVinylSd
3rd rowWd Sdng
4th rowBrkFace
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 1026
35.0%
MetalSd 450
15.4%
HdBoard 442
15.1%
Wd Sdng 420
14.3%
Plywood 221
 
7.5%
CemntBd 126
 
4.3%
BrkFace 88
 
3.0%
WdShing 56
 
1.9%
AsbShng 44
 
1.5%
Stucco 43
 
1.5%
Other values (6) 14
 
0.5%

Length

2025-01-08T16:54:22.402596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 1026
30.6%
metalsd 450
13.4%
hdboard 442
13.2%
wd 420
12.5%
sdng 420
12.5%
plywood 221
 
6.6%
cemntbd 126
 
3.8%
brkface 88
 
2.6%
wdshing 56
 
1.7%
asbshng 44
 
1.3%
Other values (7) 57
 
1.7%

Most occurring characters

ValueCountFrequency (%)
d 3603
17.6%
S 2044
 
10.0%
l 1699
 
8.3%
n 1676
 
8.2%
y 1247
 
6.1%
i 1082
 
5.3%
V 1026
 
5.0%
a 981
 
4.8%
o 937
 
4.6%
e 667
 
3.3%
Other values (22) 5499
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20461
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 3603
17.6%
S 2044
 
10.0%
l 1699
 
8.3%
n 1676
 
8.2%
y 1247
 
6.1%
i 1082
 
5.3%
V 1026
 
5.0%
a 981
 
4.8%
o 937
 
4.6%
e 667
 
3.3%
Other values (22) 5499
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20461
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 3603
17.6%
S 2044
 
10.0%
l 1699
 
8.3%
n 1676
 
8.2%
y 1247
 
6.1%
i 1082
 
5.3%
V 1026
 
5.0%
a 981
 
4.8%
o 937
 
4.6%
e 667
 
3.3%
Other values (22) 5499
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20461
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 3603
17.6%
S 2044
 
10.0%
l 1699
 
8.3%
n 1676
 
8.2%
y 1247
 
6.1%
i 1082
 
5.3%
V 1026
 
5.0%
a 981
 
4.8%
o 937
 
4.6%
e 667
 
3.3%
Other values (22) 5499
26.9%

exterior_2nd
Categorical

High correlation 

Distinct17
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
VinylSd
1015 
MetalSd
447 
HdBoard
406 
Wd Sdng
397 
Plywood
274 
Other values (12)
391 

Length

Max length7
Median length7
Mean length6.978157
Min length5

Characters and Unicode

Total characters20446
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowPlywood
2nd rowVinylSd
3rd rowWd Sdng
4th rowBrkFace
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 1015
34.6%
MetalSd 447
15.3%
HdBoard 406
 
13.9%
Wd Sdng 397
 
13.5%
Plywood 274
 
9.4%
CmentBd 126
 
4.3%
Wd Shng 81
 
2.8%
Stucco 47
 
1.6%
BrkFace 47
 
1.6%
AsbShng 38
 
1.3%
Other values (7) 52
 
1.8%

Length

2025-01-08T16:54:22.615130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 1015
29.6%
wd 478
13.9%
metalsd 447
13.0%
hdboard 406
 
11.8%
sdng 397
 
11.6%
plywood 274
 
8.0%
cmentbd 126
 
3.7%
shng 81
 
2.4%
stucco 47
 
1.4%
brkface 47
 
1.4%
Other values (9) 112
 
3.3%

Most occurring characters

ValueCountFrequency (%)
d 3549
17.4%
S 2050
 
10.0%
l 1739
 
8.5%
n 1689
 
8.3%
y 1289
 
6.3%
V 1015
 
5.0%
i 1015
 
5.0%
o 1010
 
4.9%
a 901
 
4.4%
t 643
 
3.1%
Other values (23) 5546
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 3549
17.4%
S 2050
 
10.0%
l 1739
 
8.5%
n 1689
 
8.3%
y 1289
 
6.3%
V 1015
 
5.0%
i 1015
 
5.0%
o 1010
 
4.9%
a 901
 
4.4%
t 643
 
3.1%
Other values (23) 5546
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 3549
17.4%
S 2050
 
10.0%
l 1739
 
8.5%
n 1689
 
8.3%
y 1289
 
6.3%
V 1015
 
5.0%
i 1015
 
5.0%
o 1010
 
4.9%
a 901
 
4.4%
t 643
 
3.1%
Other values (23) 5546
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 3549
17.4%
S 2050
 
10.0%
l 1739
 
8.5%
n 1689
 
8.3%
y 1289
 
6.3%
V 1015
 
5.0%
i 1015
 
5.0%
o 1010
 
4.9%
a 901
 
4.4%
t 643
 
3.1%
Other values (23) 5546
27.1%

exter_qual
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
TA
1799 
Gd
989 
Ex
 
107
Fa
 
35

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5860
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1799
61.4%
Gd 989
33.8%
Ex 107
 
3.7%
Fa 35
 
1.2%

Length

2025-01-08T16:54:22.803588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:22.935470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 1799
61.4%
gd 989
33.8%
ex 107
 
3.7%
fa 35
 
1.2%

Most occurring characters

ValueCountFrequency (%)
T 1799
30.7%
A 1799
30.7%
G 989
16.9%
d 989
16.9%
E 107
 
1.8%
x 107
 
1.8%
F 35
 
0.6%
a 35
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1799
30.7%
A 1799
30.7%
G 989
16.9%
d 989
16.9%
E 107
 
1.8%
x 107
 
1.8%
F 35
 
0.6%
a 35
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1799
30.7%
A 1799
30.7%
G 989
16.9%
d 989
16.9%
E 107
 
1.8%
x 107
 
1.8%
F 35
 
0.6%
a 35
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1799
30.7%
A 1799
30.7%
G 989
16.9%
d 989
16.9%
E 107
 
1.8%
x 107
 
1.8%
F 35
 
0.6%
a 35
 
0.6%

exter_cond
Categorical

Imbalance 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
TA
2549 
Gd
299 
Fa
 
67
Ex
 
12
Po
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5860
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 2549
87.0%
Gd 299
 
10.2%
Fa 67
 
2.3%
Ex 12
 
0.4%
Po 3
 
0.1%

Length

2025-01-08T16:54:23.100643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:23.264867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 2549
87.0%
gd 299
 
10.2%
fa 67
 
2.3%
ex 12
 
0.4%
po 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 2549
43.5%
A 2549
43.5%
G 299
 
5.1%
d 299
 
5.1%
F 67
 
1.1%
a 67
 
1.1%
E 12
 
0.2%
x 12
 
0.2%
P 3
 
0.1%
o 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 2549
43.5%
A 2549
43.5%
G 299
 
5.1%
d 299
 
5.1%
F 67
 
1.1%
a 67
 
1.1%
E 12
 
0.2%
x 12
 
0.2%
P 3
 
0.1%
o 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 2549
43.5%
A 2549
43.5%
G 299
 
5.1%
d 299
 
5.1%
F 67
 
1.1%
a 67
 
1.1%
E 12
 
0.2%
x 12
 
0.2%
P 3
 
0.1%
o 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 2549
43.5%
A 2549
43.5%
G 299
 
5.1%
d 299
 
5.1%
F 67
 
1.1%
a 67
 
1.1%
E 12
 
0.2%
x 12
 
0.2%
P 3
 
0.1%
o 3
 
0.1%

foundation
Categorical

High correlation 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
PConc
1310 
CBlock
1244 
BrkTil
311 
Slab
 
49
Stone
 
11

Length

Max length6
Median length6
Mean length5.5122867
Min length4

Characters and Unicode

Total characters16151
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCBlock
2nd rowCBlock
3rd rowCBlock
4th rowCBlock
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc 1310
44.7%
CBlock 1244
42.5%
BrkTil 311
 
10.6%
Slab 49
 
1.7%
Stone 11
 
0.4%
Wood 5
 
0.2%

Length

2025-01-08T16:54:23.475556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:23.644323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
pconc 1310
44.7%
cblock 1244
42.5%
brktil 311
 
10.6%
slab 49
 
1.7%
stone 11
 
0.4%
wood 5
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 2575
15.9%
C 2554
15.8%
c 2554
15.8%
l 1604
9.9%
k 1555
9.6%
B 1555
9.6%
n 1321
8.2%
P 1310
8.1%
r 311
 
1.9%
T 311
 
1.9%
Other values (8) 501
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2575
15.9%
C 2554
15.8%
c 2554
15.8%
l 1604
9.9%
k 1555
9.6%
B 1555
9.6%
n 1321
8.2%
P 1310
8.1%
r 311
 
1.9%
T 311
 
1.9%
Other values (8) 501
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2575
15.9%
C 2554
15.8%
c 2554
15.8%
l 1604
9.9%
k 1555
9.6%
B 1555
9.6%
n 1321
8.2%
P 1310
8.1%
r 311
 
1.9%
T 311
 
1.9%
Other values (8) 501
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2575
15.9%
C 2554
15.8%
c 2554
15.8%
l 1604
9.9%
k 1555
9.6%
B 1555
9.6%
n 1321
8.2%
P 1310
8.1%
r 311
 
1.9%
T 311
 
1.9%
Other values (8) 501
 
3.1%

bsmt_qual
Categorical

Missing 

Distinct5
Distinct (%)0.2%
Missing80
Missing (%)2.7%
Memory size3.2 KiB
TA
1283 
Gd
1219 
Ex
258 
Fa
 
88
Po
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5700
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 1283
43.8%
Gd 1219
41.6%
Ex 258
 
8.8%
Fa 88
 
3.0%
Po 2
 
0.1%
(Missing) 80
 
2.7%

Length

2025-01-08T16:54:23.869462image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:24.006587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 1283
45.0%
gd 1219
42.8%
ex 258
 
9.1%
fa 88
 
3.1%
po 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1283
22.5%
A 1283
22.5%
G 1219
21.4%
d 1219
21.4%
E 258
 
4.5%
x 258
 
4.5%
F 88
 
1.5%
a 88
 
1.5%
P 2
 
< 0.1%
o 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1283
22.5%
A 1283
22.5%
G 1219
21.4%
d 1219
21.4%
E 258
 
4.5%
x 258
 
4.5%
F 88
 
1.5%
a 88
 
1.5%
P 2
 
< 0.1%
o 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1283
22.5%
A 1283
22.5%
G 1219
21.4%
d 1219
21.4%
E 258
 
4.5%
x 258
 
4.5%
F 88
 
1.5%
a 88
 
1.5%
P 2
 
< 0.1%
o 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1283
22.5%
A 1283
22.5%
G 1219
21.4%
d 1219
21.4%
E 258
 
4.5%
x 258
 
4.5%
F 88
 
1.5%
a 88
 
1.5%
P 2
 
< 0.1%
o 2
 
< 0.1%

bsmt_cond
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.2%
Missing80
Missing (%)2.7%
Memory size3.2 KiB
TA
2616 
Gd
 
122
Fa
 
104
Po
 
5
Ex
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5700
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 2616
89.3%
Gd 122
 
4.2%
Fa 104
 
3.5%
Po 5
 
0.2%
Ex 3
 
0.1%
(Missing) 80
 
2.7%

Length

2025-01-08T16:54:24.162682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:24.305438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 2616
91.8%
gd 122
 
4.3%
fa 104
 
3.6%
po 5
 
0.2%
ex 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 2616
45.9%
A 2616
45.9%
G 122
 
2.1%
d 122
 
2.1%
F 104
 
1.8%
a 104
 
1.8%
P 5
 
0.1%
o 5
 
0.1%
E 3
 
0.1%
x 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 2616
45.9%
A 2616
45.9%
G 122
 
2.1%
d 122
 
2.1%
F 104
 
1.8%
a 104
 
1.8%
P 5
 
0.1%
o 5
 
0.1%
E 3
 
0.1%
x 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 2616
45.9%
A 2616
45.9%
G 122
 
2.1%
d 122
 
2.1%
F 104
 
1.8%
a 104
 
1.8%
P 5
 
0.1%
o 5
 
0.1%
E 3
 
0.1%
x 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 2616
45.9%
A 2616
45.9%
G 122
 
2.1%
d 122
 
2.1%
F 104
 
1.8%
a 104
 
1.8%
P 5
 
0.1%
o 5
 
0.1%
E 3
 
0.1%
x 3
 
0.1%

bsmt_exposure
Categorical

Missing 

Distinct4
Distinct (%)0.1%
Missing83
Missing (%)2.8%
Memory size3.2 KiB
No
1906 
Av
418 
Gd
284 
Mn
239 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5694
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1906
65.1%
Av 418
 
14.3%
Gd 284
 
9.7%
Mn 239
 
8.2%
(Missing) 83
 
2.8%

Length

2025-01-08T16:54:24.489912image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:24.738600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
no 1906
66.9%
av 418
 
14.7%
gd 284
 
10.0%
mn 239
 
8.4%

Most occurring characters

ValueCountFrequency (%)
N 1906
33.5%
o 1906
33.5%
A 418
 
7.3%
v 418
 
7.3%
G 284
 
5.0%
d 284
 
5.0%
M 239
 
4.2%
n 239
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1906
33.5%
o 1906
33.5%
A 418
 
7.3%
v 418
 
7.3%
G 284
 
5.0%
d 284
 
5.0%
M 239
 
4.2%
n 239
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1906
33.5%
o 1906
33.5%
A 418
 
7.3%
v 418
 
7.3%
G 284
 
5.0%
d 284
 
5.0%
M 239
 
4.2%
n 239
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1906
33.5%
o 1906
33.5%
A 418
 
7.3%
v 418
 
7.3%
G 284
 
5.0%
d 284
 
5.0%
M 239
 
4.2%
n 239
 
4.2%

bsmtfin_type_1
Categorical

Missing 

Distinct6
Distinct (%)0.2%
Missing80
Missing (%)2.7%
Memory size3.2 KiB
GLQ
859 
Unf
851 
ALQ
429 
Rec
288 
BLQ
269 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8550
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBLQ
2nd rowRec
3rd rowALQ
4th rowALQ
5th rowGLQ

Common Values

ValueCountFrequency (%)
GLQ 859
29.3%
Unf 851
29.0%
ALQ 429
14.6%
Rec 288
 
9.8%
BLQ 269
 
9.2%
LwQ 154
 
5.3%
(Missing) 80
 
2.7%

Length

2025-01-08T16:54:24.930243image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:25.081234image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
glq 859
30.1%
unf 851
29.9%
alq 429
15.1%
rec 288
 
10.1%
blq 269
 
9.4%
lwq 154
 
5.4%

Most occurring characters

ValueCountFrequency (%)
L 1711
20.0%
Q 1711
20.0%
G 859
10.0%
U 851
10.0%
n 851
10.0%
f 851
10.0%
A 429
 
5.0%
R 288
 
3.4%
e 288
 
3.4%
c 288
 
3.4%
Other values (2) 423
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 1711
20.0%
Q 1711
20.0%
G 859
10.0%
U 851
10.0%
n 851
10.0%
f 851
10.0%
A 429
 
5.0%
R 288
 
3.4%
e 288
 
3.4%
c 288
 
3.4%
Other values (2) 423
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 1711
20.0%
Q 1711
20.0%
G 859
10.0%
U 851
10.0%
n 851
10.0%
f 851
10.0%
A 429
 
5.0%
R 288
 
3.4%
e 288
 
3.4%
c 288
 
3.4%
Other values (2) 423
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 1711
20.0%
Q 1711
20.0%
G 859
10.0%
U 851
10.0%
n 851
10.0%
f 851
10.0%
A 429
 
5.0%
R 288
 
3.4%
e 288
 
3.4%
c 288
 
3.4%
Other values (2) 423
 
4.9%

bsmtfin_sf_1
Real number (ℝ)

High correlation  Zeros 

Distinct995
Distinct (%)34.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean442.62957
Minimum0
Maximum5644
Zeros930
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:25.273938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median370
Q3734
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)734

Descriptive statistics

Standard deviation455.59084
Coefficient of variation (CV)1.0292824
Kurtosis6.8592848
Mean442.62957
Median Absolute Deviation (MAD)370
Skewness1.4161822
Sum1296462
Variance207563.01
MonotonicityNot monotonic
2025-01-08T16:54:25.448592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 930
31.7%
24 27
 
0.9%
16 14
 
0.5%
300 9
 
0.3%
288 8
 
0.3%
384 8
 
0.3%
600 8
 
0.3%
20 8
 
0.3%
375 7
 
0.2%
500 7
 
0.2%
Other values (985) 1903
64.9%
ValueCountFrequency (%)
0 930
31.7%
2 1
 
< 0.1%
16 14
 
0.5%
20 8
 
0.3%
24 27
 
0.9%
25 1
 
< 0.1%
27 1
 
< 0.1%
28 5
 
0.2%
32 1
 
< 0.1%
33 1
 
< 0.1%
ValueCountFrequency (%)
5644 1
< 0.1%
4010 1
< 0.1%
2288 1
< 0.1%
2260 1
< 0.1%
2257 1
< 0.1%
2188 1
< 0.1%
2158 1
< 0.1%
2146 1
< 0.1%
2096 1
< 0.1%
2085 1
< 0.1%

bsmtfin_sf_2
Real number (ℝ)

Zeros 

Distinct274
Distinct (%)9.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean49.722431
Minimum0
Maximum1526
Zeros2578
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:25.615366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile435
Maximum1526
Range1526
Interquartile range (IQR)0

Descriptive statistics

Standard deviation169.16848
Coefficient of variation (CV)3.4022567
Kurtosis18.781481
Mean49.722431
Median Absolute Deviation (MAD)0
Skewness4.1399785
Sum145637
Variance28617.973
MonotonicityNot monotonic
2025-01-08T16:54:26.274212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2578
88.0%
180 5
 
0.2%
294 5
 
0.2%
182 3
 
0.1%
168 3
 
0.1%
435 3
 
0.1%
72 3
 
0.1%
374 3
 
0.1%
147 3
 
0.1%
539 3
 
0.1%
Other values (264) 320
 
10.9%
ValueCountFrequency (%)
0 2578
88.0%
6 1
 
< 0.1%
12 1
 
< 0.1%
28 1
 
< 0.1%
32 1
 
< 0.1%
35 1
 
< 0.1%
38 1
 
< 0.1%
40 2
 
0.1%
41 2
 
0.1%
42 2
 
0.1%
ValueCountFrequency (%)
1526 1
< 0.1%
1474 1
< 0.1%
1393 1
< 0.1%
1164 1
< 0.1%
1127 1
< 0.1%
1120 1
< 0.1%
1085 1
< 0.1%
1083 1
< 0.1%
1080 1
< 0.1%
1073 1
< 0.1%

bsmt_unf_sf
Real number (ℝ)

High correlation  Zeros 

Distinct1137
Distinct (%)38.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean559.26255
Minimum0
Maximum2336
Zeros244
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:26.505491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1219
median466
Q3802
95-th percentile1473.6
Maximum2336
Range2336
Interquartile range (IQR)583

Descriptive statistics

Standard deviation439.49415
Coefficient of variation (CV)0.78584585
Kurtosis0.40952533
Mean559.26255
Median Absolute Deviation (MAD)280
Skewness0.92305274
Sum1638080
Variance193155.11
MonotonicityNot monotonic
2025-01-08T16:54:26.682792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 244
 
8.3%
384 19
 
0.6%
728 14
 
0.5%
672 13
 
0.4%
600 12
 
0.4%
816 11
 
0.4%
572 11
 
0.4%
216 11
 
0.4%
100 11
 
0.4%
624 10
 
0.3%
Other values (1127) 2573
87.8%
ValueCountFrequency (%)
0 244
8.3%
14 1
 
< 0.1%
15 1
 
< 0.1%
17 1
 
< 0.1%
20 1
 
< 0.1%
22 1
 
< 0.1%
23 2
 
0.1%
25 3
 
0.1%
26 1
 
< 0.1%
27 1
 
< 0.1%
ValueCountFrequency (%)
2336 1
< 0.1%
2153 1
< 0.1%
2140 1
< 0.1%
2121 1
< 0.1%
2062 1
< 0.1%
2046 1
< 0.1%
2042 1
< 0.1%
2002 1
< 0.1%
1969 1
< 0.1%
1967 1
< 0.1%

total_bsmt_sf
Real number (ℝ)

High correlation  Zeros 

Distinct1058
Distinct (%)36.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1051.6145
Minimum0
Maximum6110
Zeros79
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:26.853458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile453
Q1793
median990
Q31302
95-th percentile1776
Maximum6110
Range6110
Interquartile range (IQR)509

Descriptive statistics

Standard deviation440.61507
Coefficient of variation (CV)0.41898913
Kurtosis9.1356123
Mean1051.6145
Median Absolute Deviation (MAD)236
Skewness1.1562043
Sum3080179
Variance194141.64
MonotonicityNot monotonic
2025-01-08T16:54:27.073507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79
 
2.7%
864 74
 
2.5%
672 29
 
1.0%
912 26
 
0.9%
1040 25
 
0.9%
768 24
 
0.8%
816 23
 
0.8%
728 21
 
0.7%
384 19
 
0.6%
1008 19
 
0.6%
Other values (1048) 2590
88.4%
ValueCountFrequency (%)
0 79
2.7%
105 1
 
< 0.1%
160 1
 
< 0.1%
173 1
 
< 0.1%
190 1
 
< 0.1%
192 1
 
< 0.1%
216 2
 
0.1%
240 1
 
< 0.1%
245 1
 
< 0.1%
264 4
 
0.1%
ValueCountFrequency (%)
6110 1
< 0.1%
5095 1
< 0.1%
3206 1
< 0.1%
3200 1
< 0.1%
3138 1
< 0.1%
3094 1
< 0.1%
2846 1
< 0.1%
2660 1
< 0.1%
2633 1
< 0.1%
2630 1
< 0.1%

heating_qc
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Ex
1495 
TA
864 
Gd
476 
Fa
 
92
Po
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5860
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFa
2nd rowTA
3rd rowTA
4th rowEx
5th rowGd

Common Values

ValueCountFrequency (%)
Ex 1495
51.0%
TA 864
29.5%
Gd 476
 
16.2%
Fa 92
 
3.1%
Po 3
 
0.1%

Length

2025-01-08T16:54:27.237140image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:27.378156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ex 1495
51.0%
ta 864
29.5%
gd 476
 
16.2%
fa 92
 
3.1%
po 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 1495
25.5%
x 1495
25.5%
T 864
14.7%
A 864
14.7%
G 476
 
8.1%
d 476
 
8.1%
F 92
 
1.6%
a 92
 
1.6%
P 3
 
0.1%
o 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1495
25.5%
x 1495
25.5%
T 864
14.7%
A 864
14.7%
G 476
 
8.1%
d 476
 
8.1%
F 92
 
1.6%
a 92
 
1.6%
P 3
 
0.1%
o 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1495
25.5%
x 1495
25.5%
T 864
14.7%
A 864
14.7%
G 476
 
8.1%
d 476
 
8.1%
F 92
 
1.6%
a 92
 
1.6%
P 3
 
0.1%
o 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1495
25.5%
x 1495
25.5%
T 864
14.7%
A 864
14.7%
G 476
 
8.1%
d 476
 
8.1%
F 92
 
1.6%
a 92
 
1.6%
P 3
 
0.1%
o 3
 
0.1%

central_air
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Y
2734 
N
 
196

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2930
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 2734
93.3%
N 196
 
6.7%

Length

2025-01-08T16:54:27.573490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:27.766539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
y 2734
93.3%
n 196
 
6.7%

Most occurring characters

ValueCountFrequency (%)
Y 2734
93.3%
N 196
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 2734
93.3%
N 196
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 2734
93.3%
N 196
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 2734
93.3%
N 196
 
6.7%

electrical
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size3.2 KiB
SBrkr
2682 
FuseA
 
188
FuseF
 
50
FuseP
 
8
Mix
 
1

Length

Max length5
Median length5
Mean length4.9993172
Min length3

Characters and Unicode

Total characters14643
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr

Common Values

ValueCountFrequency (%)
SBrkr 2682
91.5%
FuseA 188
 
6.4%
FuseF 50
 
1.7%
FuseP 8
 
0.3%
Mix 1
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2025-01-08T16:54:27.966470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:28.156318image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
sbrkr 2682
91.6%
fusea 188
 
6.4%
fusef 50
 
1.7%
fusep 8
 
0.3%
mix 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 5364
36.6%
S 2682
18.3%
B 2682
18.3%
k 2682
18.3%
F 296
 
2.0%
u 246
 
1.7%
s 246
 
1.7%
e 246
 
1.7%
A 188
 
1.3%
P 8
 
0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 5364
36.6%
S 2682
18.3%
B 2682
18.3%
k 2682
18.3%
F 296
 
2.0%
u 246
 
1.7%
s 246
 
1.7%
e 246
 
1.7%
A 188
 
1.3%
P 8
 
0.1%
Other values (3) 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 5364
36.6%
S 2682
18.3%
B 2682
18.3%
k 2682
18.3%
F 296
 
2.0%
u 246
 
1.7%
s 246
 
1.7%
e 246
 
1.7%
A 188
 
1.3%
P 8
 
0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 5364
36.6%
S 2682
18.3%
B 2682
18.3%
k 2682
18.3%
F 296
 
2.0%
u 246
 
1.7%
s 246
 
1.7%
e 246
 
1.7%
A 188
 
1.3%
P 8
 
0.1%
Other values (3) 3
 
< 0.1%

1st_flr_sf
Real number (ℝ)

High correlation 

Distinct1083
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.5577
Minimum334
Maximum5095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:28.406466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile665.45
Q1876.25
median1084
Q31384
95-th percentile1829.55
Maximum5095
Range4761
Interquartile range (IQR)507.75

Descriptive statistics

Standard deviation391.89089
Coefficient of variation (CV)0.33796584
Kurtosis6.9688085
Mean1159.5577
Median Absolute Deviation (MAD)236
Skewness1.4694286
Sum3397504
Variance153578.47
MonotonicityNot monotonic
2025-01-08T16:54:28.607595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 46
 
1.6%
1040 28
 
1.0%
912 19
 
0.6%
816 18
 
0.6%
960 18
 
0.6%
848 18
 
0.6%
894 17
 
0.6%
936 17
 
0.6%
672 17
 
0.6%
546 15
 
0.5%
Other values (1073) 2717
92.7%
ValueCountFrequency (%)
334 1
 
< 0.1%
372 1
 
< 0.1%
407 1
 
< 0.1%
432 1
 
< 0.1%
438 1
 
< 0.1%
442 1
 
< 0.1%
448 1
 
< 0.1%
453 1
 
< 0.1%
480 1
 
< 0.1%
483 13
0.4%
ValueCountFrequency (%)
5095 1
< 0.1%
4692 1
< 0.1%
3820 1
< 0.1%
3228 1
< 0.1%
3138 1
< 0.1%
2898 1
< 0.1%
2726 1
< 0.1%
2696 1
< 0.1%
2674 1
< 0.1%
2633 1
< 0.1%

2nd_flr_sf
Real number (ℝ)

High correlation  Zeros 

Distinct635
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean335.45597
Minimum0
Maximum2065
Zeros1678
Zeros (%)57.3%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:28.785481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3703.75
95-th percentile1130.1
Maximum2065
Range2065
Interquartile range (IQR)703.75

Descriptive statistics

Standard deviation428.39572
Coefficient of variation (CV)1.277055
Kurtosis-0.4148613
Mean335.45597
Median Absolute Deviation (MAD)0
Skewness0.86645675
Sum982886
Variance183522.89
MonotonicityNot monotonic
2025-01-08T16:54:29.003563image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1678
57.3%
546 23
 
0.8%
728 18
 
0.6%
504 17
 
0.6%
600 13
 
0.4%
672 13
 
0.4%
720 13
 
0.4%
896 11
 
0.4%
886 10
 
0.3%
780 9
 
0.3%
Other values (625) 1125
38.4%
ValueCountFrequency (%)
0 1678
57.3%
110 1
 
< 0.1%
125 1
 
< 0.1%
144 1
 
< 0.1%
167 1
 
< 0.1%
180 1
 
< 0.1%
182 1
 
< 0.1%
185 1
 
< 0.1%
192 1
 
< 0.1%
208 2
 
0.1%
ValueCountFrequency (%)
2065 1
< 0.1%
1872 1
< 0.1%
1862 1
< 0.1%
1836 1
< 0.1%
1818 1
< 0.1%
1796 1
< 0.1%
1788 1
< 0.1%
1778 1
< 0.1%
1721 1
< 0.1%
1629 1
< 0.1%

gr_liv_area
Real number (ℝ)

High correlation 

Distinct1292
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1499.6904
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:29.198457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile861
Q11126
median1442
Q31742.75
95-th percentile2463.1
Maximum5642
Range5308
Interquartile range (IQR)616.75

Descriptive statistics

Standard deviation505.50889
Coefficient of variation (CV)0.33707549
Kurtosis4.1378382
Mean1499.6904
Median Absolute Deviation (MAD)311
Skewness1.2741097
Sum4394093
Variance255539.24
MonotonicityNot monotonic
2025-01-08T16:54:29.414349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 41
 
1.4%
1092 26
 
0.9%
1040 25
 
0.9%
1456 20
 
0.7%
1200 18
 
0.6%
894 15
 
0.5%
912 14
 
0.5%
816 14
 
0.5%
1728 13
 
0.4%
848 13
 
0.4%
Other values (1282) 2731
93.2%
ValueCountFrequency (%)
334 1
< 0.1%
407 1
< 0.1%
438 1
< 0.1%
480 1
< 0.1%
492 1
< 0.1%
498 1
< 0.1%
520 1
< 0.1%
540 1
< 0.1%
572 1
< 0.1%
599 1
< 0.1%
ValueCountFrequency (%)
5642 1
< 0.1%
5095 1
< 0.1%
4676 1
< 0.1%
4476 1
< 0.1%
4316 1
< 0.1%
3820 1
< 0.1%
3672 1
< 0.1%
3627 1
< 0.1%
3608 1
< 0.1%
3500 1
< 0.1%

bsmt_full_bath
Categorical

Distinct4
Distinct (%)0.1%
Missing2
Missing (%)0.1%
Memory size23.0 KiB
0.0
1707 
1.0
1181 
2.0
 
38
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8784
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1707
58.3%
1.0 1181
40.3%
2.0 38
 
1.3%
3.0 2
 
0.1%
(Missing) 2
 
0.1%

Length

2025-01-08T16:54:29.622322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:29.764785image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1707
58.3%
1.0 1181
40.3%
2.0 38
 
1.3%
3.0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 4635
52.8%
. 2928
33.3%
1 1181
 
13.4%
2 38
 
0.4%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8784
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4635
52.8%
. 2928
33.3%
1 1181
 
13.4%
2 38
 
0.4%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8784
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4635
52.8%
. 2928
33.3%
1 1181
 
13.4%
2 38
 
0.4%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8784
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4635
52.8%
. 2928
33.3%
1 1181
 
13.4%
2 38
 
0.4%
3 2
 
< 0.1%

bsmt_half_bath
Categorical

Imbalance 

Distinct3
Distinct (%)0.1%
Missing2
Missing (%)0.1%
Memory size23.0 KiB
0.0
2753 
1.0
 
171
2.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8784
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2753
94.0%
1.0 171
 
5.8%
2.0 4
 
0.1%
(Missing) 2
 
0.1%

Length

2025-01-08T16:54:29.925454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:30.059203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2753
94.0%
1.0 171
 
5.8%
2.0 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 5681
64.7%
. 2928
33.3%
1 171
 
1.9%
2 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8784
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5681
64.7%
. 2928
33.3%
1 171
 
1.9%
2 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8784
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5681
64.7%
. 2928
33.3%
1 171
 
1.9%
2 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8784
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5681
64.7%
. 2928
33.3%
1 171
 
1.9%
2 4
 
< 0.1%

full_bath
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
2
1532 
1
1318 
3
 
64
0
 
12
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2930
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 1532
52.3%
1 1318
45.0%
3 64
 
2.2%
0 12
 
0.4%
4 4
 
0.1%

Length

2025-01-08T16:54:30.239491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:30.407483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2 1532
52.3%
1 1318
45.0%
3 64
 
2.2%
0 12
 
0.4%
4 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 1532
52.3%
1 1318
45.0%
3 64
 
2.2%
0 12
 
0.4%
4 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1532
52.3%
1 1318
45.0%
3 64
 
2.2%
0 12
 
0.4%
4 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1532
52.3%
1 1318
45.0%
3 64
 
2.2%
0 12
 
0.4%
4 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1532
52.3%
1 1318
45.0%
3 64
 
2.2%
0 12
 
0.4%
4 4
 
0.1%

half_bath
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
0
1843 
1
1062 
2
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2930
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1843
62.9%
1 1062
36.2%
2 25
 
0.9%

Length

2025-01-08T16:54:30.569445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:30.715450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1843
62.9%
1 1062
36.2%
2 25
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 1843
62.9%
1 1062
36.2%
2 25
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1843
62.9%
1 1062
36.2%
2 25
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1843
62.9%
1 1062
36.2%
2 25
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1843
62.9%
1 1062
36.2%
2 25
 
0.9%

bedroom_abvgr
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8542662
Minimum0
Maximum8
Zeros8
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:30.896430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.82773114
Coefficient of variation (CV)0.28999788
Kurtosis1.8914207
Mean2.8542662
Median Absolute Deviation (MAD)0
Skewness0.30569421
Sum8363
Variance0.68513884
MonotonicityNot monotonic
2025-01-08T16:54:31.064426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 1597
54.5%
2 743
25.4%
4 400
 
13.7%
1 112
 
3.8%
5 48
 
1.6%
6 21
 
0.7%
0 8
 
0.3%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 8
 
0.3%
1 112
 
3.8%
2 743
25.4%
3 1597
54.5%
4 400
 
13.7%
5 48
 
1.6%
6 21
 
0.7%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 21
 
0.7%
5 48
 
1.6%
4 400
 
13.7%
3 1597
54.5%
2 743
25.4%
1 112
 
3.8%
0 8
 
0.3%

kitchen_abvgr
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
1
2796 
2
 
129
0
 
3
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2930
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2796
95.4%
2 129
 
4.4%
0 3
 
0.1%
3 2
 
0.1%

Length

2025-01-08T16:54:31.246508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:31.371843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 2796
95.4%
2 129
 
4.4%
0 3
 
0.1%
3 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 2796
95.4%
2 129
 
4.4%
0 3
 
0.1%
3 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2796
95.4%
2 129
 
4.4%
0 3
 
0.1%
3 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2796
95.4%
2 129
 
4.4%
0 3
 
0.1%
3 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2796
95.4%
2 129
 
4.4%
0 3
 
0.1%
3 2
 
0.1%

kitchen_qual
Categorical

High correlation 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
TA
1494 
Gd
1160 
Ex
205 
Fa
 
70
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5860
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTA
2nd rowTA
3rd rowGd
4th rowEx
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1494
51.0%
Gd 1160
39.6%
Ex 205
 
7.0%
Fa 70
 
2.4%
Po 1
 
< 0.1%

Length

2025-01-08T16:54:31.657346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:31.866738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 1494
51.0%
gd 1160
39.6%
ex 205
 
7.0%
fa 70
 
2.4%
po 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 1494
25.5%
A 1494
25.5%
G 1160
19.8%
d 1160
19.8%
E 205
 
3.5%
x 205
 
3.5%
F 70
 
1.2%
a 70
 
1.2%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1494
25.5%
A 1494
25.5%
G 1160
19.8%
d 1160
19.8%
E 205
 
3.5%
x 205
 
3.5%
F 70
 
1.2%
a 70
 
1.2%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1494
25.5%
A 1494
25.5%
G 1160
19.8%
d 1160
19.8%
E 205
 
3.5%
x 205
 
3.5%
F 70
 
1.2%
a 70
 
1.2%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1494
25.5%
A 1494
25.5%
G 1160
19.8%
d 1160
19.8%
E 205
 
3.5%
x 205
 
3.5%
F 70
 
1.2%
a 70
 
1.2%
P 1
 
< 0.1%
o 1
 
< 0.1%

totrms_abvgrd
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4430034
Minimum2
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:32.018610image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile9
Maximum15
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5729644
Coefficient of variation (CV)0.24413527
Kurtosis1.1545882
Mean6.4430034
Median Absolute Deviation (MAD)1
Skewness0.75354256
Sum18878
Variance2.474217
MonotonicityNot monotonic
2025-01-08T16:54:32.227702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
6 844
28.8%
7 649
22.2%
5 586
20.0%
8 347
11.8%
4 203
 
6.9%
9 143
 
4.9%
10 80
 
2.7%
11 32
 
1.1%
3 26
 
0.9%
12 16
 
0.5%
Other values (4) 4
 
0.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 26
 
0.9%
4 203
 
6.9%
5 586
20.0%
6 844
28.8%
7 649
22.2%
8 347
11.8%
9 143
 
4.9%
10 80
 
2.7%
11 32
 
1.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 16
 
0.5%
11 32
 
1.1%
10 80
 
2.7%
9 143
 
4.9%
8 347
11.8%
7 649
22.2%
6 844
28.8%

functional
Categorical

Imbalance 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Typ
2728 
Min2
 
70
Min1
 
65
Mod
 
35
Maj1
 
19
Other values (3)
 
13

Length

Max length4
Median length3
Mean length3.0556314
Min length3

Characters and Unicode

Total characters8953
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTyp
2nd rowTyp
3rd rowTyp
4th rowTyp
5th rowTyp

Common Values

ValueCountFrequency (%)
Typ 2728
93.1%
Min2 70
 
2.4%
Min1 65
 
2.2%
Mod 35
 
1.2%
Maj1 19
 
0.6%
Maj2 9
 
0.3%
Sal 2
 
0.1%
Sev 2
 
0.1%

Length

2025-01-08T16:54:32.450363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:32.605403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
typ 2728
93.1%
min2 70
 
2.4%
min1 65
 
2.2%
mod 35
 
1.2%
maj1 19
 
0.6%
maj2 9
 
0.3%
sal 2
 
0.1%
sev 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 2728
30.5%
y 2728
30.5%
p 2728
30.5%
M 198
 
2.2%
i 135
 
1.5%
n 135
 
1.5%
1 84
 
0.9%
2 79
 
0.9%
o 35
 
0.4%
d 35
 
0.4%
Other values (6) 68
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8953
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 2728
30.5%
y 2728
30.5%
p 2728
30.5%
M 198
 
2.2%
i 135
 
1.5%
n 135
 
1.5%
1 84
 
0.9%
2 79
 
0.9%
o 35
 
0.4%
d 35
 
0.4%
Other values (6) 68
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8953
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 2728
30.5%
y 2728
30.5%
p 2728
30.5%
M 198
 
2.2%
i 135
 
1.5%
n 135
 
1.5%
1 84
 
0.9%
2 79
 
0.9%
o 35
 
0.4%
d 35
 
0.4%
Other values (6) 68
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8953
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 2728
30.5%
y 2728
30.5%
p 2728
30.5%
M 198
 
2.2%
i 135
 
1.5%
n 135
 
1.5%
1 84
 
0.9%
2 79
 
0.9%
o 35
 
0.4%
d 35
 
0.4%
Other values (6) 68
 
0.8%

fireplaces
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
0
1422 
1
1274 
2
221 
3
 
12
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2930
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row0
3rd row0
4th row2
5th row1

Common Values

ValueCountFrequency (%)
0 1422
48.5%
1 1274
43.5%
2 221
 
7.5%
3 12
 
0.4%
4 1
 
< 0.1%

Length

2025-01-08T16:54:32.852098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:33.035518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1422
48.5%
1 1274
43.5%
2 221
 
7.5%
3 12
 
0.4%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1422
48.5%
1 1274
43.5%
2 221
 
7.5%
3 12
 
0.4%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1422
48.5%
1 1274
43.5%
2 221
 
7.5%
3 12
 
0.4%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1422
48.5%
1 1274
43.5%
2 221
 
7.5%
3 12
 
0.4%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1422
48.5%
1 1274
43.5%
2 221
 
7.5%
3 12
 
0.4%
4 1
 
< 0.1%

fireplace_qu
Categorical

Missing 

Distinct5
Distinct (%)0.3%
Missing1422
Missing (%)48.5%
Memory size3.2 KiB
Gd
744 
TA
600 
Fa
75 
Po
 
46
Ex
 
43

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3016
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
Gd 744
25.4%
TA 600
20.5%
Fa 75
 
2.6%
Po 46
 
1.6%
Ex 43
 
1.5%
(Missing) 1422
48.5%

Length

2025-01-08T16:54:33.203454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:33.383730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
gd 744
49.3%
ta 600
39.8%
fa 75
 
5.0%
po 46
 
3.1%
ex 43
 
2.9%

Most occurring characters

ValueCountFrequency (%)
G 744
24.7%
d 744
24.7%
T 600
19.9%
A 600
19.9%
F 75
 
2.5%
a 75
 
2.5%
P 46
 
1.5%
o 46
 
1.5%
E 43
 
1.4%
x 43
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 744
24.7%
d 744
24.7%
T 600
19.9%
A 600
19.9%
F 75
 
2.5%
a 75
 
2.5%
P 46
 
1.5%
o 46
 
1.5%
E 43
 
1.4%
x 43
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 744
24.7%
d 744
24.7%
T 600
19.9%
A 600
19.9%
F 75
 
2.5%
a 75
 
2.5%
P 46
 
1.5%
o 46
 
1.5%
E 43
 
1.4%
x 43
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 744
24.7%
d 744
24.7%
T 600
19.9%
A 600
19.9%
F 75
 
2.5%
a 75
 
2.5%
P 46
 
1.5%
o 46
 
1.5%
E 43
 
1.4%
x 43
 
1.4%

garage_type
Categorical

Missing 

Distinct6
Distinct (%)0.2%
Missing157
Missing (%)5.4%
Memory size3.2 KiB
Attchd
1731 
Detchd
782 
BuiltIn
186 
Basment
 
36
2Types
 
23

Length

Max length7
Median length6
Mean length6.085467
Min length6

Characters and Unicode

Total characters16875
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowAttchd
5th rowAttchd

Common Values

ValueCountFrequency (%)
Attchd 1731
59.1%
Detchd 782
26.7%
BuiltIn 186
 
6.3%
Basment 36
 
1.2%
2Types 23
 
0.8%
CarPort 15
 
0.5%
(Missing) 157
 
5.4%

Length

2025-01-08T16:54:33.559892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:33.765876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
attchd 1731
62.4%
detchd 782
28.2%
builtin 186
 
6.7%
basment 36
 
1.3%
2types 23
 
0.8%
carport 15
 
0.5%

Most occurring characters

ValueCountFrequency (%)
t 4481
26.6%
c 2513
14.9%
h 2513
14.9%
d 2513
14.9%
A 1731
 
10.3%
e 841
 
5.0%
D 782
 
4.6%
B 222
 
1.3%
n 222
 
1.3%
u 186
 
1.1%
Other values (14) 871
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 4481
26.6%
c 2513
14.9%
h 2513
14.9%
d 2513
14.9%
A 1731
 
10.3%
e 841
 
5.0%
D 782
 
4.6%
B 222
 
1.3%
n 222
 
1.3%
u 186
 
1.1%
Other values (14) 871
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 4481
26.6%
c 2513
14.9%
h 2513
14.9%
d 2513
14.9%
A 1731
 
10.3%
e 841
 
5.0%
D 782
 
4.6%
B 222
 
1.3%
n 222
 
1.3%
u 186
 
1.1%
Other values (14) 871
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 4481
26.6%
c 2513
14.9%
h 2513
14.9%
d 2513
14.9%
A 1731
 
10.3%
e 841
 
5.0%
D 782
 
4.6%
B 222
 
1.3%
n 222
 
1.3%
u 186
 
1.1%
Other values (14) 871
 
5.2%

garage_yr_blt
Real number (ℝ)

High correlation  Missing 

Distinct103
Distinct (%)3.7%
Missing159
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean1978.1324
Minimum1895
Maximum2207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:33.999838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1895
5-th percentile1928
Q11960
median1979
Q32002
95-th percentile2007
Maximum2207
Range312
Interquartile range (IQR)42

Descriptive statistics

Standard deviation25.528411
Coefficient of variation (CV)0.012905309
Kurtosis1.8265782
Mean1978.1324
Median Absolute Deviation (MAD)21
Skewness-0.38467176
Sum5481405
Variance651.69978
MonotonicityNot monotonic
2025-01-08T16:54:34.258390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 142
 
4.8%
2006 115
 
3.9%
2007 115
 
3.9%
2004 99
 
3.4%
2003 92
 
3.1%
1977 66
 
2.3%
2008 61
 
2.1%
1998 59
 
2.0%
2000 55
 
1.9%
1999 54
 
1.8%
Other values (93) 1913
65.3%
(Missing) 159
 
5.4%
ValueCountFrequency (%)
1895 1
 
< 0.1%
1896 1
 
< 0.1%
1900 6
0.2%
1906 1
 
< 0.1%
1908 1
 
< 0.1%
1910 10
0.3%
1914 2
 
0.1%
1915 7
0.2%
1916 6
0.2%
1917 2
 
0.1%
ValueCountFrequency (%)
2207 1
 
< 0.1%
2010 5
 
0.2%
2009 29
 
1.0%
2008 61
2.1%
2007 115
3.9%
2006 115
3.9%
2005 142
4.8%
2004 99
3.4%
2003 92
3.1%
2002 53
 
1.8%

garage_finish
Categorical

Missing 

Distinct3
Distinct (%)0.1%
Missing159
Missing (%)5.4%
Memory size3.1 KiB
Unf
1231 
RFn
812 
Fin
728 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8313
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFin
2nd rowUnf
3rd rowUnf
4th rowFin
5th rowFin

Common Values

ValueCountFrequency (%)
Unf 1231
42.0%
RFn 812
27.7%
Fin 728
24.8%
(Missing) 159
 
5.4%

Length

2025-01-08T16:54:34.502481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:34.699248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
unf 1231
44.4%
rfn 812
29.3%
fin 728
26.3%

Most occurring characters

ValueCountFrequency (%)
n 2771
33.3%
F 1540
18.5%
U 1231
14.8%
f 1231
14.8%
R 812
 
9.8%
i 728
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2771
33.3%
F 1540
18.5%
U 1231
14.8%
f 1231
14.8%
R 812
 
9.8%
i 728
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2771
33.3%
F 1540
18.5%
U 1231
14.8%
f 1231
14.8%
R 812
 
9.8%
i 728
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2771
33.3%
F 1540
18.5%
U 1231
14.8%
f 1231
14.8%
R 812
 
9.8%
i 728
 
8.8%

garage_cars
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.7668146
Minimum0
Maximum5
Zeros157
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:34.854422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.76056636
Coefficient of variation (CV)0.43047321
Kurtosis0.24496945
Mean1.7668146
Median Absolute Deviation (MAD)0
Skewness-0.21983636
Sum5175
Variance0.5784612
MonotonicityNot monotonic
2025-01-08T16:54:35.045351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 1603
54.7%
1 778
26.6%
3 374
 
12.8%
0 157
 
5.4%
4 16
 
0.5%
5 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 157
 
5.4%
1 778
26.6%
2 1603
54.7%
3 374
 
12.8%
4 16
 
0.5%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 16
 
0.5%
3 374
 
12.8%
2 1603
54.7%
1 778
26.6%
0 157
 
5.4%

garage_area
Real number (ℝ)

High correlation  Zeros 

Distinct603
Distinct (%)20.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean472.81973
Minimum0
Maximum1488
Zeros157
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:35.302630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1320
median480
Q3576
95-th percentile856
Maximum1488
Range1488
Interquartile range (IQR)256

Descriptive statistics

Standard deviation215.04655
Coefficient of variation (CV)0.4548172
Kurtosis0.95102299
Mean472.81973
Median Absolute Deviation (MAD)123
Skewness0.24199424
Sum1384889
Variance46245.018
MonotonicityNot monotonic
2025-01-08T16:54:35.527653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 157
 
5.4%
576 97
 
3.3%
440 96
 
3.3%
484 76
 
2.6%
240 69
 
2.4%
528 65
 
2.2%
400 58
 
2.0%
480 54
 
1.8%
264 51
 
1.7%
288 50
 
1.7%
Other values (593) 2156
73.6%
ValueCountFrequency (%)
0 157
5.4%
100 1
 
< 0.1%
160 3
 
0.1%
162 2
 
0.1%
164 2
 
0.1%
180 16
 
0.5%
184 1
 
< 0.1%
185 1
 
< 0.1%
186 1
 
< 0.1%
189 1
 
< 0.1%
ValueCountFrequency (%)
1488 1
< 0.1%
1418 1
< 0.1%
1390 1
< 0.1%
1356 1
< 0.1%
1348 1
< 0.1%
1314 1
< 0.1%
1248 1
< 0.1%
1231 1
< 0.1%
1220 1
< 0.1%
1200 1
< 0.1%

garage_qual
Categorical

Imbalance  Missing 

Distinct5
Distinct (%)0.2%
Missing159
Missing (%)5.4%
Memory size3.2 KiB
TA
2615 
Fa
 
124
Gd
 
24
Po
 
5
Ex
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5542
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 2615
89.2%
Fa 124
 
4.2%
Gd 24
 
0.8%
Po 5
 
0.2%
Ex 3
 
0.1%
(Missing) 159
 
5.4%

Length

2025-01-08T16:54:35.723739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:35.915248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 2615
94.4%
fa 124
 
4.5%
gd 24
 
0.9%
po 5
 
0.2%
ex 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 2615
47.2%
A 2615
47.2%
F 124
 
2.2%
a 124
 
2.2%
G 24
 
0.4%
d 24
 
0.4%
P 5
 
0.1%
o 5
 
0.1%
E 3
 
0.1%
x 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 2615
47.2%
A 2615
47.2%
F 124
 
2.2%
a 124
 
2.2%
G 24
 
0.4%
d 24
 
0.4%
P 5
 
0.1%
o 5
 
0.1%
E 3
 
0.1%
x 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 2615
47.2%
A 2615
47.2%
F 124
 
2.2%
a 124
 
2.2%
G 24
 
0.4%
d 24
 
0.4%
P 5
 
0.1%
o 5
 
0.1%
E 3
 
0.1%
x 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 2615
47.2%
A 2615
47.2%
F 124
 
2.2%
a 124
 
2.2%
G 24
 
0.4%
d 24
 
0.4%
P 5
 
0.1%
o 5
 
0.1%
E 3
 
0.1%
x 3
 
0.1%

garage_cond
Categorical

Imbalance  Missing 

Distinct5
Distinct (%)0.2%
Missing159
Missing (%)5.4%
Memory size3.2 KiB
TA
2665 
Fa
 
74
Gd
 
15
Po
 
14
Ex
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5542
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 2665
91.0%
Fa 74
 
2.5%
Gd 15
 
0.5%
Po 14
 
0.5%
Ex 3
 
0.1%
(Missing) 159
 
5.4%

Length

2025-01-08T16:54:36.113581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:36.278397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 2665
96.2%
fa 74
 
2.7%
gd 15
 
0.5%
po 14
 
0.5%
ex 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 2665
48.1%
A 2665
48.1%
F 74
 
1.3%
a 74
 
1.3%
G 15
 
0.3%
d 15
 
0.3%
P 14
 
0.3%
o 14
 
0.3%
E 3
 
0.1%
x 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 2665
48.1%
A 2665
48.1%
F 74
 
1.3%
a 74
 
1.3%
G 15
 
0.3%
d 15
 
0.3%
P 14
 
0.3%
o 14
 
0.3%
E 3
 
0.1%
x 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 2665
48.1%
A 2665
48.1%
F 74
 
1.3%
a 74
 
1.3%
G 15
 
0.3%
d 15
 
0.3%
P 14
 
0.3%
o 14
 
0.3%
E 3
 
0.1%
x 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 2665
48.1%
A 2665
48.1%
F 74
 
1.3%
a 74
 
1.3%
G 15
 
0.3%
d 15
 
0.3%
P 14
 
0.3%
o 14
 
0.3%
E 3
 
0.1%
x 3
 
0.1%

paved_drive
Categorical

Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Y
2652 
N
 
216
P
 
62

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2930
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 2652
90.5%
N 216
 
7.4%
P 62
 
2.1%

Length

2025-01-08T16:54:36.433419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:36.618459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
y 2652
90.5%
n 216
 
7.4%
p 62
 
2.1%

Most occurring characters

ValueCountFrequency (%)
Y 2652
90.5%
N 216
 
7.4%
P 62
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 2652
90.5%
N 216
 
7.4%
P 62
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 2652
90.5%
N 216
 
7.4%
P 62
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 2652
90.5%
N 216
 
7.4%
P 62
 
2.1%

wood_deck_sf
Real number (ℝ)

Zeros 

Distinct380
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.751877
Minimum0
Maximum1424
Zeros1526
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:36.771068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile327.55
Maximum1424
Range1424
Interquartile range (IQR)168

Descriptive statistics

Standard deviation126.36156
Coefficient of variation (CV)1.3478297
Kurtosis6.7539552
Mean93.751877
Median Absolute Deviation (MAD)0
Skewness1.8426781
Sum274693
Variance15967.244
MonotonicityNot monotonic
2025-01-08T16:54:36.970345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1526
52.1%
100 74
 
2.5%
192 70
 
2.4%
144 61
 
2.1%
168 56
 
1.9%
120 53
 
1.8%
140 29
 
1.0%
240 20
 
0.7%
224 19
 
0.6%
160 17
 
0.6%
Other values (370) 1005
34.3%
ValueCountFrequency (%)
0 1526
52.1%
4 1
 
< 0.1%
12 2
 
0.1%
14 1
 
< 0.1%
16 1
 
< 0.1%
20 1
 
< 0.1%
22 1
 
< 0.1%
23 1
 
< 0.1%
24 5
 
0.2%
25 2
 
0.1%
ValueCountFrequency (%)
1424 1
< 0.1%
870 1
< 0.1%
857 1
< 0.1%
736 1
< 0.1%
728 1
< 0.1%
690 1
< 0.1%
684 1
< 0.1%
670 1
< 0.1%
668 1
< 0.1%
657 1
< 0.1%

open_porch_sf
Real number (ℝ)

Zeros 

Distinct252
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.533447
Minimum0
Maximum742
Zeros1300
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:37.220162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median27
Q370
95-th percentile182.55
Maximum742
Range742
Interquartile range (IQR)70

Descriptive statistics

Standard deviation67.4834
Coefficient of variation (CV)1.4197035
Kurtosis10.954343
Mean47.533447
Median Absolute Deviation (MAD)27
Skewness2.5353859
Sum139273
Variance4554.0093
MonotonicityNot monotonic
2025-01-08T16:54:37.505491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1300
44.4%
36 52
 
1.8%
48 52
 
1.8%
40 44
 
1.5%
32 38
 
1.3%
24 36
 
1.2%
28 35
 
1.2%
20 33
 
1.1%
30 31
 
1.1%
60 30
 
1.0%
Other values (242) 1279
43.7%
ValueCountFrequency (%)
0 1300
44.4%
4 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
10 2
 
0.1%
11 3
 
0.1%
12 5
 
0.2%
15 2
 
0.1%
16 15
 
0.5%
17 2
 
0.1%
ValueCountFrequency (%)
742 1
< 0.1%
570 1
< 0.1%
547 1
< 0.1%
523 1
< 0.1%
502 1
< 0.1%
484 1
< 0.1%
444 1
< 0.1%
418 1
< 0.1%
406 1
< 0.1%
382 1
< 0.1%

pool_qc
Categorical

High correlation  Missing 

Distinct4
Distinct (%)30.8%
Missing2917
Missing (%)99.6%
Memory size3.2 KiB
Ex
Gd
TA
Fa

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters26
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEx
2nd rowGd
3rd rowGd
4th rowEx
5th rowTA

Common Values

ValueCountFrequency (%)
Ex 4
 
0.1%
Gd 4
 
0.1%
TA 3
 
0.1%
Fa 2
 
0.1%
(Missing) 2917
99.6%

Length

2025-01-08T16:54:37.756268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:37.957466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ex 4
30.8%
gd 4
30.8%
ta 3
23.1%
fa 2
15.4%

Most occurring characters

ValueCountFrequency (%)
E 4
15.4%
x 4
15.4%
G 4
15.4%
d 4
15.4%
T 3
11.5%
A 3
11.5%
F 2
7.7%
a 2
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 4
15.4%
x 4
15.4%
G 4
15.4%
d 4
15.4%
T 3
11.5%
A 3
11.5%
F 2
7.7%
a 2
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 4
15.4%
x 4
15.4%
G 4
15.4%
d 4
15.4%
T 3
11.5%
A 3
11.5%
F 2
7.7%
a 2
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 4
15.4%
x 4
15.4%
G 4
15.4%
d 4
15.4%
T 3
11.5%
A 3
11.5%
F 2
7.7%
a 2
7.7%

fence
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.7%
Missing2358
Missing (%)80.5%
Memory size3.2 KiB
MnPrv
330 
GdPrv
118 
GdWo
112 
MnWw
 
12

Length

Max length5
Median length5
Mean length4.7832168
Min length4

Characters and Unicode

Total characters2736
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMnPrv
2nd rowMnPrv
3rd rowGdPrv
4th rowMnPrv
5th rowMnPrv

Common Values

ValueCountFrequency (%)
MnPrv 330
 
11.3%
GdPrv 118
 
4.0%
GdWo 112
 
3.8%
MnWw 12
 
0.4%
(Missing) 2358
80.5%

Length

2025-01-08T16:54:38.143599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T16:54:38.323871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
mnprv 330
57.7%
gdprv 118
 
20.6%
gdwo 112
 
19.6%
mnww 12
 
2.1%

Most occurring characters

ValueCountFrequency (%)
r 448
16.4%
P 448
16.4%
v 448
16.4%
M 342
12.5%
n 342
12.5%
G 230
8.4%
d 230
8.4%
W 124
 
4.5%
o 112
 
4.1%
w 12
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 448
16.4%
P 448
16.4%
v 448
16.4%
M 342
12.5%
n 342
12.5%
G 230
8.4%
d 230
8.4%
W 124
 
4.5%
o 112
 
4.1%
w 12
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 448
16.4%
P 448
16.4%
v 448
16.4%
M 342
12.5%
n 342
12.5%
G 230
8.4%
d 230
8.4%
W 124
 
4.5%
o 112
 
4.1%
w 12
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 448
16.4%
P 448
16.4%
v 448
16.4%
M 342
12.5%
n 342
12.5%
G 230
8.4%
d 230
8.4%
W 124
 
4.5%
o 112
 
4.1%
w 12
 
0.4%

saleprice
Real number (ℝ)

High correlation 

Distinct1032
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180796.06
Minimum12789
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.0 KiB
2025-01-08T16:54:38.498005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum12789
5-th percentile87500
Q1129500
median160000
Q3213500
95-th percentile335000
Maximum755000
Range742211
Interquartile range (IQR)84000

Descriptive statistics

Standard deviation79886.692
Coefficient of variation (CV)0.4418608
Kurtosis5.1189
Mean180796.06
Median Absolute Deviation (MAD)37000
Skewness1.7435001
Sum5.2973246 × 108
Variance6.3818836 × 109
MonotonicityNot monotonic
2025-01-08T16:54:39.010205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135000 34
 
1.2%
140000 33
 
1.1%
130000 29
 
1.0%
155000 28
 
1.0%
145000 26
 
0.9%
160000 23
 
0.8%
110000 21
 
0.7%
185000 21
 
0.7%
127000 20
 
0.7%
120000 20
 
0.7%
Other values (1022) 2675
91.3%
ValueCountFrequency (%)
12789 1
< 0.1%
13100 1
< 0.1%
34900 1
< 0.1%
35000 1
< 0.1%
35311 1
< 0.1%
37900 1
< 0.1%
39300 1
< 0.1%
40000 1
< 0.1%
44000 1
< 0.1%
45000 1
< 0.1%
ValueCountFrequency (%)
755000 1
< 0.1%
745000 1
< 0.1%
625000 1
< 0.1%
615000 1
< 0.1%
611657 1
< 0.1%
610000 1
< 0.1%
591587 1
< 0.1%
584500 1
< 0.1%
582933 1
< 0.1%
556581 1
< 0.1%

Interactions

2025-01-08T16:54:08.930538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:52:51.323265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:52:54.040588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:52:57.271396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:00.181520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:03.323647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:07.162057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:10.828663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:14.322610image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:18.980747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:23.005654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:26.130605image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:30.735866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:34.699416image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:37.990529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:41.164542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:44.479561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:49.789534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-08T16:54:12.395796image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:52:53.646299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:52:56.818464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:52:59.812575image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:02.664632image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:06.685535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:10.328895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:13.811972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:17.935180image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:22.416903image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:25.651603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:30.075239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:34.181448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:37.549376image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:40.713527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:44.015309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:49.012438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:52.882108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:56.614660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:59.953869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:54:04.349711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:54:08.298076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:54:12.615389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:52:53.762725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:52:56.938557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:52:59.923575image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:03.090967image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:06.854893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:10.529275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:13.948589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:18.219816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:22.665653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:25.853667image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:30.377247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:34.370743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:37.693349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:40.866421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:44.164420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:49.242490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:53.058372image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:56.745445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:54:00.102680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:54:04.516567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:54:08.580469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:54:12.796357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:52:53.916588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:52:57.140598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:00.041468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:03.207519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:06.996764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:10.685294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:14.108397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:18.417820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:22.858270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:26.013438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:30.574131image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:34.528492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:37.815564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:41.005671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:44.346420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:49.499485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:53.301277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:53:56.880383image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:54:00.306610image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:54:04.709492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-08T16:54:08.736783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-08T16:54:39.301423image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
1st_flr_sf2nd_flr_sfbedroom_abvgrbldg_typebsmt_condbsmt_exposurebsmt_full_bathbsmt_half_bathbsmt_qualbsmt_unf_sfbsmtfin_sf_1bsmtfin_sf_2bsmtfin_type_1central_aircondition_1electricalexter_condexter_qualexterior_1stexterior_2ndfencefireplace_qufireplacesfoundationfull_bathfunctionalgarage_areagarage_carsgarage_condgarage_finishgarage_qualgarage_typegarage_yr_bltgr_liv_areahalf_bathheating_qchouse_stylekitchen_abvgrkitchen_qualland_contourlot_arealot_configlot_shapems_subclassms_zoningneighborhoodopen_porch_sfoverall_condoverall_qualpaved_drivepidpool_qcroof_stylesalepricestreettotal_bsmt_sftotrms_abvgrdutilitieswood_deck_sfyear_builtyear_remod_add
1st_flr_sf1.000-0.3260.1140.1540.0200.2020.1630.0310.2340.2190.3340.0560.1250.1660.0640.0510.0000.2860.1760.1110.0510.1260.2370.1280.2080.0000.4880.4490.0860.2520.1000.1700.2640.4910.1360.1000.1630.0250.2320.1010.4390.0320.150-0.2780.1230.2270.251-0.1870.4160.141-0.1300.0000.1550.5820.0000.8280.3420.0000.2060.3250.250
2nd_flr_sf-0.3261.0000.5020.1500.0560.1510.1560.0330.2020.052-0.203-0.1180.1290.1370.0480.0580.0480.2010.1050.1470.1510.1360.1360.1680.3730.0460.0900.1520.0390.2410.1020.2530.0810.6040.4600.1240.4320.0450.1590.0450.0650.0380.1470.4780.1460.2570.201-0.0130.2380.113-0.0690.0000.1330.2470.000-0.3130.5550.0000.0660.0230.090
bedroom_abvgr0.1140.5021.0000.3130.0630.1160.2420.0060.0830.168-0.100-0.0240.0970.1580.0530.0630.0000.1220.0700.0780.0610.0700.0750.0750.4350.0160.1140.1220.0340.0870.0370.130-0.0430.5260.2780.0370.2360.2610.0930.1070.2990.0500.0630.0570.1150.2130.075-0.0120.0780.091-0.0140.1550.1480.1970.0130.0560.6660.0000.029-0.032-0.032
bldg_type0.1540.1500.3131.0000.0530.0730.1990.0650.1400.1350.0290.0000.1170.2710.0820.0950.1100.1830.1710.2120.0710.0440.1100.1750.1350.0530.1560.1820.0410.1740.0470.1430.1370.0740.2450.1150.1570.5010.1360.0700.0320.0760.0800.8530.1890.4340.0560.1140.1450.1320.1341.0000.0450.0990.0420.1370.2190.0000.0600.2400.208
bsmt_cond0.0200.0560.0630.0531.0000.0610.0870.0230.1470.0230.0290.0290.1000.2320.0460.2330.1620.1670.0920.0830.0000.0430.0190.1240.0880.1220.0920.0900.1330.1050.1750.0760.1180.0000.0450.0910.0750.0160.1120.0480.0000.0300.0430.0830.0940.1350.0000.1810.2490.1600.0751.0000.0530.1040.0330.0310.0000.0000.0240.1570.104
bsmt_exposure0.2020.1510.1160.0730.0611.0000.1940.0680.2280.1240.2420.0590.2140.0940.0760.0680.0410.1850.1340.1480.0000.0810.1360.1340.0960.0270.1710.1770.0470.2060.0590.1540.1490.1040.0500.0760.2370.0360.1620.1900.1010.0810.1200.2160.0920.2800.0490.1060.2190.0860.0900.2720.1310.2300.0330.2030.0870.0000.1440.1870.163
bsmt_full_bath0.1630.1560.2420.1990.0870.1941.0000.1050.1340.2480.3740.0970.3430.1140.0360.0610.0260.1020.0770.0800.0000.0550.1160.1200.2750.0000.1420.1960.0600.1410.0880.1410.1130.1010.1820.0610.1760.1680.1120.0780.1210.0360.0640.2400.0830.1860.1010.0500.1260.1010.0740.0000.1200.1700.0240.1980.0740.0000.1610.1490.123
bsmt_half_bath0.0310.0330.0060.0650.0230.0680.1051.0000.0460.0690.0650.1040.0900.0360.0000.0000.0410.0580.1130.0900.0480.0700.3530.0820.1020.0450.0550.0780.0000.0180.0000.0000.0710.0500.0860.0360.1160.4090.0280.0470.0000.0470.0220.1160.0190.1480.0000.0720.0610.0180.0290.0000.1560.0440.0000.0430.0540.0460.1060.1090.085
bsmt_qual0.2340.2020.0830.1400.1470.2280.1340.0461.0000.1270.2170.0460.3030.2680.1360.1450.1070.4680.2970.2860.0950.2200.1610.3730.2850.0790.3020.3500.1310.4150.2040.2450.3760.2240.1760.2350.1950.0650.3830.1230.0000.0870.1660.2360.2290.4780.1050.2800.4490.2310.1550.0000.1710.4370.0520.2580.1480.0000.1450.4630.350
bsmt_unf_sf0.2190.0520.1680.1350.0230.1240.2480.0690.1271.000-0.547-0.2980.2880.0940.0360.0580.0530.1920.0910.0990.0000.1510.0440.1700.1580.0360.1060.1390.0380.1380.0670.0940.1780.2490.1080.1000.1520.0970.1360.0540.0680.0290.033-0.1140.0570.1670.146-0.1230.2390.117-0.1310.3930.0690.1630.0000.3310.2510.000-0.0470.1240.167
bsmtfin_sf_10.334-0.203-0.1000.0290.0290.2420.3740.0650.217-0.5471.0000.0580.2770.1420.0800.0650.0000.2320.1170.1130.0000.1240.2040.1310.1090.0000.2480.2040.0300.1950.0740.1220.1190.0720.0270.0760.1120.0260.1970.1250.1710.0430.173-0.0980.0770.1870.095-0.0210.1790.125-0.0500.1920.1090.3320.0000.428-0.0440.0000.2080.2150.088
bsmtfin_sf_20.056-0.118-0.0240.0000.0290.0590.0970.1040.046-0.2980.0581.0000.1720.0280.0000.0000.0470.0440.0690.0790.0000.0790.0900.0740.0680.073-0.013-0.0600.0000.0350.0000.032-0.141-0.0770.0190.0380.0400.0020.0280.0600.0570.0420.029-0.0930.0000.116-0.0560.096-0.0920.0310.0130.0960.088-0.0340.0000.065-0.0870.0800.061-0.096-0.117
bsmtfin_type_10.1250.1290.0970.1170.1000.2140.3430.0900.3030.2880.2770.1721.0000.1900.0640.1030.0650.3050.2250.2240.1090.1410.1130.3040.2000.0730.1710.1980.0820.2710.1020.1560.3150.1120.0720.1980.1620.0560.2420.0870.0000.0680.0930.1890.1240.3180.0690.1780.2440.1960.1080.2530.0640.2210.0000.1490.0880.0000.1050.3470.264
central_air0.1660.1370.1580.2710.2320.0940.1140.0360.2680.0940.1420.0280.1901.0000.1130.3950.2430.3050.3300.3050.0420.0650.1830.3450.1220.1390.3270.3280.3030.2410.2940.2750.3390.1870.1500.3500.1740.2180.3100.1290.0000.0720.1230.2610.2920.3760.0920.3020.3870.3840.2151.0000.1050.4110.0550.2270.1340.0370.1280.4220.371
condition_10.0640.0480.0530.0820.0460.0760.0360.0000.1360.0360.0800.0000.0640.1131.0000.0650.0510.1210.0780.0670.0250.0500.0650.0790.0800.0000.0610.0650.0000.1390.0250.0900.1140.0600.0980.0730.0720.0460.0790.0520.0000.1180.1030.0880.0740.1950.0250.0430.0840.1170.0760.0000.0470.0910.1400.0720.0540.0000.0400.1110.076
electrical0.0510.0580.0630.0950.2330.0680.0610.0000.1450.0580.0650.0000.1030.3950.0651.0000.1120.1390.1720.1760.0470.0340.0690.1490.0920.1680.1120.1280.1740.1720.2620.1180.2020.0630.0740.1440.1120.0770.1490.0540.0000.0240.0880.1320.1280.1770.0200.2060.1580.1800.0981.0000.0000.1580.0000.0720.0810.0780.0580.1970.214
exter_cond0.0000.0480.0000.1100.1620.0410.0260.0410.1070.0530.0000.0470.0650.2430.0510.1121.0000.2390.1110.0810.0390.0000.0770.1160.1560.2300.0860.1240.1400.1060.0870.0720.1080.0350.0970.1860.0710.0390.1130.0390.0000.0260.0310.1140.2310.1610.1000.3450.2070.1770.0830.0000.0370.1360.0520.0450.0000.0460.0620.1610.108
exter_qual0.2860.2010.1220.1830.1670.1850.1020.0580.4680.1920.2320.0440.3050.3050.1210.1390.2391.0000.3460.3370.0980.2250.1830.3710.3040.1690.3390.3610.0910.3740.0990.2350.3990.2690.1680.3320.1830.0940.5440.1220.0000.0350.1310.2650.2430.4970.1460.3050.6140.1990.1270.4060.1640.5100.2380.3290.1860.0780.1620.4410.406
exterior_1st0.1760.1050.0700.1710.0920.1340.0770.1130.2970.0910.1170.0690.2250.3300.0780.1720.1110.3461.0000.7410.0000.2010.1390.3120.2040.1010.1440.2160.1350.3220.1330.2000.3990.1120.1550.2640.1590.1240.2630.1070.0550.0410.1050.1840.2620.2860.0480.1790.2300.2210.2170.0000.1490.1730.0030.1230.0820.0000.0840.3330.294
exterior_2nd0.1110.1470.0780.2120.0830.1480.0800.0900.2860.0990.1130.0790.2240.3050.0670.1760.0810.3370.7411.0000.0000.1860.1170.3140.2000.0720.1470.2110.1230.3180.1100.2060.4040.1220.1670.2640.1620.1330.2550.1070.0510.1160.1120.2050.1650.3350.0550.1670.2000.2010.2150.0000.1700.1800.0000.1260.0850.0350.0830.3250.289
fence0.0510.1510.0610.0710.0000.0000.0000.0480.0950.0000.0000.0000.1090.0420.0250.0470.0390.0980.0000.0001.0000.0000.0730.1070.1210.0000.1160.1170.0000.0970.0000.0610.1170.1130.1140.0600.1110.0000.1170.0000.1180.0680.1050.1360.0330.1940.0320.0000.1430.0000.1740.0000.0000.1480.0000.0310.1341.0000.1220.1580.131
fireplace_qu0.1260.1360.0700.0440.0430.0810.0550.0700.2200.1510.1240.0790.1410.0650.0500.0340.0000.2250.2010.1860.0001.0000.0110.1660.1090.0720.1530.1630.0000.1080.0190.0870.1970.1790.1130.1500.1640.0040.2220.0870.0000.0340.0260.1620.0600.3010.0300.0880.2080.0730.0670.0000.0690.2170.0000.1450.1390.0000.0290.2780.311
fireplaces0.2370.1360.0750.1100.0190.1360.1160.3530.1610.0440.2040.0900.1130.1830.0650.0690.0770.1830.1390.1170.0730.0111.0000.1150.1550.0840.1710.1860.0480.2420.0540.1800.1140.2750.1680.0980.0940.0910.1500.1110.1040.0540.1280.1700.1140.2760.0680.1050.2250.1300.0900.0000.0830.2730.0290.2240.1650.0000.1850.1570.128
foundation0.1280.1680.0750.1750.1240.1340.1200.0820.3730.1700.1310.0740.3040.3450.0790.1490.1160.3710.3120.3140.1070.1660.1151.0000.2530.1150.2040.2350.1280.3790.1990.2430.4650.1650.1890.2900.2150.1460.2930.0990.0000.0430.1360.2550.2220.4240.0880.2540.2880.2510.2170.4770.0970.2700.0870.2250.1110.0000.0960.5050.331
full_bath0.2080.3730.4350.1350.0880.0960.2750.1020.2850.1580.1090.0680.2000.1220.0800.0920.1560.3040.2040.2000.1210.1090.1550.2531.0000.0390.2440.3400.0510.3180.0740.2170.2790.3850.3440.1770.1940.1480.2330.1020.0590.0450.1230.2310.1420.3240.1150.2270.3060.1140.1060.0000.1310.3520.0480.1980.2910.0000.1580.3040.244
functional0.0000.0460.0160.0530.1220.0270.0000.0450.0790.0360.0000.0730.0730.1390.0000.1680.2300.1690.1010.0720.0000.0720.0840.1150.0391.0000.0230.0340.1150.0880.0890.1300.0980.0330.0000.2130.0500.0000.1080.0760.0460.0000.0000.0730.2220.0810.0850.2020.1710.1190.1440.4300.0740.0670.0000.0260.0250.0690.0680.0900.054
garage_area0.4880.0900.1140.1560.0920.1710.1420.0550.3020.1060.248-0.0130.1710.3270.0610.1120.0860.3390.1440.1470.1160.1530.1710.2040.2440.0231.0000.8640.1200.2960.1460.1670.5990.4870.1360.1650.1120.1210.2710.0850.3710.0720.153-0.0480.1460.2670.335-0.2160.5470.284-0.2070.5000.1060.6610.1250.4810.3340.0000.2630.5340.409
garage_cars0.4490.1520.1220.1820.0900.1770.1960.0780.3500.1390.204-0.0600.1980.3280.0650.1280.1240.3610.2160.2110.1170.1630.1860.2350.3400.0340.8641.0000.0740.3260.1160.2030.6380.5240.2220.1910.1500.1590.3050.0810.3440.0600.1480.0200.1510.3630.337-0.2580.6110.289-0.2770.1800.1210.7020.0000.4500.3850.0000.2740.6010.461
garage_cond0.0860.0390.0340.0410.1330.0470.0600.0000.1310.0380.0300.0000.0820.3030.0000.1740.1400.0910.1350.1230.0000.0000.0480.1280.0510.1150.1200.0741.0000.1380.4920.1220.1890.0840.0600.1010.1020.1580.1170.0000.0000.0380.0500.0620.1400.1410.0000.1600.1720.2610.1050.0000.0560.1530.0000.0320.0000.0000.0250.1740.111
garage_finish0.2520.2410.0870.1740.1050.2060.1410.0180.4150.1380.1950.0350.2710.2410.1390.1720.1060.3740.3220.3180.0970.1080.2420.3790.3180.0880.2960.3260.1381.0000.1630.4590.3880.2580.1720.2750.2400.1020.3400.1230.0220.0660.1950.3220.2290.4870.1490.2800.3970.1820.2210.1120.1210.4240.0000.2610.1790.0130.1670.4570.353
garage_qual0.1000.1020.0370.0470.1750.0590.0880.0000.2040.0670.0740.0000.1020.2940.0250.2620.0870.0990.1330.1100.0000.0190.0540.1990.0740.0890.1460.1160.4920.1631.0000.1280.2330.1600.0430.0740.1640.0440.1060.0410.0220.0160.0690.1470.1390.1860.0800.1300.1410.2200.1140.0000.0460.1660.0000.0780.0880.0000.0260.2280.133
garage_type0.1700.2530.1300.1430.0760.1540.1410.0000.2450.0940.1220.0320.1560.2750.0900.1180.0720.2350.2000.2060.0610.0870.1800.2430.2170.1300.1670.2030.1220.4590.1281.0000.2310.1910.2160.1390.2350.1200.1870.0900.0410.0590.1620.2800.1880.3250.0780.1690.2150.2420.1990.0000.0820.2390.1090.1780.1610.1100.1000.2920.214
garage_yr_blt0.2640.081-0.0430.1370.1180.1490.1130.0710.3760.1780.119-0.1410.3150.3390.1140.2020.1080.3990.3990.4040.1170.1970.1140.4650.2790.0980.5990.6380.1890.3880.2330.2311.0000.3260.1950.3130.2470.0750.3130.1230.0740.0700.1560.0840.2160.4860.401-0.3950.6380.261-0.2740.3490.0870.6380.0390.3650.2100.0000.2800.9000.742
gr_liv_area0.4910.6040.5260.0740.0000.1040.1010.0500.2240.2490.072-0.0770.1120.1870.0600.0630.0350.2690.1120.1220.1130.1790.2750.1650.3850.0330.4870.5240.0840.2580.1600.1910.3261.0000.3040.1520.2340.0480.2270.0970.4180.0450.1870.1870.0790.2130.402-0.1890.5780.157-0.1650.0960.0710.7230.0770.3790.8080.0000.2290.3170.319
half_bath0.1360.4600.2780.2450.0450.0500.1820.0860.1760.1080.0270.0190.0720.1500.0980.0740.0970.1680.1550.1670.1140.1130.1680.1890.3440.0000.1360.2220.0600.1720.0430.2160.1950.3041.0000.1410.4550.2100.1650.0000.0000.0480.1060.5090.1250.3160.1170.1270.2280.0970.1170.5580.1450.2340.0080.0860.2590.0000.0860.2370.211
heating_qc0.1000.1240.0370.1150.0910.0760.0610.0360.2350.1000.0760.0380.1980.3500.0730.1440.1860.3320.2640.2640.0600.1500.0980.2900.1770.2130.1650.1910.1010.2750.0740.1390.3130.1520.1411.0000.1320.0980.2840.0680.0000.0250.0810.1750.2390.3060.0960.1760.2970.1550.0500.2690.0310.2540.0510.1370.0950.0180.0960.3360.327
house_style0.1630.4320.2360.1570.0750.2370.1760.1160.1950.1520.1120.0400.1620.1740.0720.1120.0710.1830.1590.1620.1110.1640.0940.2150.1940.0500.1120.1500.1020.2400.1640.2350.2470.2340.4550.1321.0000.1180.1350.1130.0000.0470.1030.6180.1520.2860.0870.1210.1490.1680.2230.3870.1110.1330.0000.1530.2370.0410.0560.2780.202
kitchen_abvgr0.0250.0450.2610.5010.0160.0360.1680.4090.0650.0970.0260.0020.0560.2180.0460.0770.0390.0940.1240.1330.0000.0040.0910.1460.1480.0000.1210.1590.1580.1020.0440.1200.0750.0480.2100.0980.1181.0000.1050.0000.0000.0430.0490.4940.0690.1060.0570.0650.0960.1020.0431.0000.1450.0700.0000.0570.2200.0000.0230.1490.115
kitchen_qual0.2320.1590.0930.1360.1120.1620.1120.0280.3830.1360.1970.0280.2420.3100.0790.1490.1130.5440.2630.2550.1170.2220.1500.2930.2330.1080.2710.3050.1170.3400.1060.1870.3130.2270.1650.2840.1350.1051.0000.1060.0000.0000.1140.2050.1540.3910.1070.2110.4730.1900.1170.1950.1290.4230.0700.2510.1770.0000.1380.3430.361
land_contour0.1010.0450.1070.0700.0480.1900.0780.0470.1230.0540.1250.0600.0870.1290.0520.0540.0390.1220.1070.1070.0000.0870.1110.0990.1020.0760.0850.0810.0000.1230.0410.0900.1230.0970.0000.0680.1130.0000.1061.0000.1970.0470.1150.1100.0970.3620.0000.0790.1660.0630.1600.1110.1280.1350.0980.1120.0980.0300.1120.1510.120
lot_area0.4390.0650.2990.0320.0000.1010.1210.0000.0000.0680.1710.0570.0000.0000.0000.0000.0000.0000.0550.0510.1180.0000.1040.0000.0590.0460.3710.3440.0000.0220.0220.0410.0740.4180.0000.0000.0000.0000.0000.1971.0000.0740.207-0.3210.0830.1230.172-0.0790.1970.063-0.0400.1490.0840.4290.2020.3530.3840.0620.1780.1210.103
lot_config0.0320.0380.0500.0760.0300.0810.0360.0470.0870.0290.0430.0420.0680.0720.1180.0240.0260.0350.0410.1160.0680.0340.0540.0430.0450.0000.0720.0600.0380.0660.0160.0590.0700.0450.0480.0250.0470.0430.0000.0470.0741.0000.2180.0810.0650.1550.0000.0210.0340.0450.0000.1460.0560.0720.0000.0150.0280.0630.0390.1110.087
lot_shape0.1500.1470.0630.0800.0430.1200.0640.0220.1660.0330.1730.0290.0930.1230.1030.0880.0310.1310.1050.1120.1050.0260.1280.1360.1230.0000.1530.1480.0500.1950.0690.1620.1560.1870.1060.0810.1030.0490.1140.1150.2070.2181.0000.1560.1500.2600.0350.0900.1570.1110.0930.0000.0200.2010.0000.1780.0740.0000.0900.1960.164
ms_subclass-0.2780.4780.0570.8530.0830.2160.2400.1160.236-0.114-0.098-0.0930.1890.2610.0880.1320.1140.2650.1840.2050.1360.1620.1700.2550.2310.073-0.0480.0200.0620.3220.1470.2800.0840.1870.5090.1750.6180.4940.2050.110-0.3210.0810.1561.0000.2140.4040.031-0.0660.1030.172-0.0270.2190.1170.0020.032-0.3010.1370.0000.0190.0360.016
ms_zoning0.1230.1460.1150.1890.0940.0920.0830.0190.2290.0570.0770.0000.1240.2920.0740.1280.2310.2430.2620.1650.0330.0600.1140.2220.1420.2220.1460.1510.1400.2290.1390.1880.2160.0790.1250.2390.1520.0690.1540.0970.0830.0650.1500.2141.0000.5360.0970.1770.3390.2580.4931.0000.0680.1950.3470.1160.0960.3510.0620.2620.168
neighborhood0.2270.2570.2130.4340.1350.2800.1860.1480.4780.1670.1870.1160.3180.3760.1950.1770.1610.4970.2860.3350.1940.3010.2760.4240.3240.0810.2670.3630.1410.4870.1860.3250.4860.2130.3160.3060.2860.1060.3910.3620.1230.1550.2600.4040.5361.0000.1110.2240.3380.3590.7530.4300.2070.3360.2230.2380.1830.0380.1500.4830.398
open_porch_sf0.2510.2010.0750.0560.0000.0490.1010.0000.1050.1460.095-0.0560.0690.0920.0250.0200.1000.1460.0480.0550.0320.0300.0680.0880.1150.0850.3350.3370.0000.1490.0800.0780.4010.4020.1170.0960.0870.0570.1070.0000.1720.0000.0350.0310.0970.1111.000-0.1750.4400.000-0.1770.0000.0000.4800.0000.2760.2590.0000.1120.4030.372
overall_cond-0.187-0.013-0.0120.1140.1810.1060.0500.0720.280-0.123-0.0210.0960.1780.3020.0430.2060.3450.3050.1790.1670.0000.0880.1050.2540.2270.202-0.216-0.2580.1600.2800.1300.169-0.395-0.1890.1270.1760.1210.0650.2110.079-0.0790.0210.090-0.0660.1770.224-0.1751.000-0.1900.2390.1120.0000.072-0.1660.064-0.223-0.1260.159-0.036-0.422-0.075
overall_qual0.4160.2380.0780.1450.2490.2190.1260.0610.4490.2390.179-0.0920.2440.3870.0840.1580.2070.6140.2300.2000.1430.2080.2250.2880.3060.1710.5470.6110.1720.3970.1410.2150.6380.5780.2280.2970.1490.0960.4730.1660.1970.0340.1570.1030.3390.3380.440-0.1901.0000.263-0.3140.2720.1430.8090.1080.4730.3780.1290.2900.6650.579
paved_drive0.1410.1130.0910.1320.1600.0860.1010.0180.2310.1170.1250.0310.1960.3840.1170.1800.1770.1990.2210.2010.0000.0730.1300.2510.1140.1190.2840.2890.2610.1820.2200.2420.2610.1570.0970.1550.1680.1020.1900.0630.0630.0450.1110.1720.2580.3590.0000.2390.2631.0000.1920.2520.1030.2950.1060.1600.0900.0160.0790.3750.221
pid-0.130-0.069-0.0140.1340.0750.0900.0740.0290.155-0.131-0.0500.0130.1080.2150.0760.0980.0830.1270.2170.2150.1740.0670.0900.2170.1060.144-0.207-0.2770.1050.2210.1140.199-0.274-0.1650.1170.0500.2230.0430.1170.160-0.0400.0000.093-0.0270.4930.753-0.1770.112-0.3140.1921.0000.0000.085-0.2710.129-0.188-0.1300.040-0.092-0.315-0.208
pool_qc0.0000.0000.1551.0001.0000.2720.0000.0000.0000.3930.1920.0960.2531.0000.0001.0000.0000.4060.0000.0000.0000.0000.0000.4770.0000.4300.5000.1800.0000.1120.0000.0000.3490.0960.5580.2690.3871.0000.1950.1110.1490.1460.0000.2191.0000.4300.0000.0000.2720.2520.0001.0000.0000.0001.0000.0000.0001.0000.1110.4010.000
roof_style0.1550.1330.1480.0450.0530.1310.1200.1560.1710.0690.1090.0880.0640.1050.0470.0000.0370.1640.1490.1700.0000.0690.0830.0970.1310.0740.1060.1210.0560.1210.0460.0820.0870.0710.1450.0310.1110.1450.1290.1280.0840.0560.0200.1170.0680.2070.0000.0720.1430.1030.0850.0001.0000.1400.0000.1310.0880.0000.0690.1520.091
saleprice0.5820.2470.1970.0990.1040.2300.1700.0440.4370.1630.332-0.0340.2210.4110.0910.1580.1360.5100.1730.1800.1480.2170.2730.2700.3520.0670.6610.7020.1530.4240.1660.2390.6380.7230.2340.2540.1330.0700.4230.1350.4290.0720.2010.0020.1950.3360.480-0.1660.8090.295-0.2710.0000.1401.0000.1220.6060.4990.0000.3640.6810.601
street0.0000.0000.0130.0420.0330.0330.0240.0000.0520.0000.0000.0000.0000.0550.1400.0000.0520.2380.0030.0000.0000.0000.0290.0870.0480.0000.1250.0000.0000.0000.0000.1090.0390.0770.0080.0510.0000.0000.0700.0980.2020.0000.0000.0320.3470.2230.0000.0640.1080.1060.1291.0000.0000.1221.0000.0110.0310.2010.0750.0760.095
total_bsmt_sf0.828-0.3130.0560.1370.0310.2030.1980.0430.2580.3310.4280.0650.1490.2270.0720.0720.0450.3290.1230.1260.0310.1450.2240.2250.1980.0260.4810.4500.0320.2610.0780.1780.3650.3790.0860.1370.1530.0570.2510.1120.3530.0150.178-0.3010.1160.2380.276-0.2230.4730.160-0.1880.0000.1310.6060.0111.0000.2330.0000.2240.4420.310
totrms_abvgrd0.3420.5550.6660.2190.0000.0870.0740.0540.1480.251-0.044-0.0870.0880.1340.0540.0810.0000.1860.0820.0850.1340.1390.1650.1110.2910.0250.3340.3850.0000.1790.0880.1610.2100.8080.2590.0950.2370.2200.1770.0980.3840.0280.0740.1370.0960.1830.259-0.1260.3780.090-0.1300.0000.0880.4990.0310.2331.0000.0000.1580.1820.208
utilities0.0000.0000.0000.0000.0000.0000.0000.0460.0000.0000.0000.0800.0000.0370.0000.0780.0460.0780.0000.0351.0000.0000.0000.0000.0000.0690.0000.0000.0000.0130.0000.1100.0000.0000.0000.0180.0410.0000.0000.0300.0620.0630.0000.0000.3510.0380.0000.1590.1290.0160.0401.0000.0000.0000.2010.0000.0001.0000.0000.0350.039
wood_deck_sf0.2060.0660.0290.0600.0240.1440.1610.1060.145-0.0470.2080.0610.1050.1280.0400.0580.0620.1620.0840.0830.1220.0290.1850.0960.1580.0680.2630.2740.0250.1670.0260.1000.2800.2290.0860.0960.0560.0230.1380.1120.1780.0390.0900.0190.0620.1500.112-0.0360.2900.079-0.0920.1110.0690.3640.0750.2240.1580.0001.0000.2970.250
year_built0.3250.023-0.0320.2400.1570.1870.1490.1090.4630.1240.215-0.0960.3470.4220.1110.1970.1610.4410.3330.3250.1580.2780.1570.5050.3040.0900.5340.6010.1740.4570.2280.2920.9000.3170.2370.3360.2780.1490.3430.1510.1210.1110.1960.0360.2620.4830.403-0.4220.6650.375-0.3150.4010.1520.6810.0760.4420.1820.0350.2971.0000.708
year_remod_add0.2500.090-0.0320.2080.1040.1630.1230.0850.3500.1670.088-0.1170.2640.3710.0760.2140.1080.4060.2940.2890.1310.3110.1280.3310.2440.0540.4090.4610.1110.3530.1330.2140.7420.3190.2110.3270.2020.1150.3610.1200.1030.0870.1640.0160.1680.3980.372-0.0750.5790.221-0.2080.0000.0910.6010.0950.3100.2080.0390.2500.7081.000

Missing values

2025-01-08T16:54:13.179606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-08T16:54:13.748553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-08T16:54:14.737879image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

pidms_subclassms_zoninglot_areastreetlot_shapeland_contourutilitieslot_configneighborhoodcondition_1bldg_typehouse_styleoverall_qualoverall_condyear_builtyear_remod_addroof_styleexterior_1stexterior_2ndexter_qualexter_condfoundationbsmt_qualbsmt_condbsmt_exposurebsmtfin_type_1bsmtfin_sf_1bsmtfin_sf_2bsmt_unf_sftotal_bsmt_sfheating_qccentral_airelectrical1st_flr_sf2nd_flr_sfgr_liv_areabsmt_full_bathbsmt_half_bathfull_bathhalf_bathbedroom_abvgrkitchen_abvgrkitchen_qualtotrms_abvgrdfunctionalfireplacesfireplace_qugarage_typegarage_yr_bltgarage_finishgarage_carsgarage_areagarage_qualgarage_condpaved_drivewood_deck_sfopen_porch_sfpool_qcfencesaleprice
052630110020RL31770PaveIR1LvlAllPubCornerNAmesNorm1Fam1Story6519601960HipBrkFacePlywoodTATACBlockTAGdGdBLQ639.00.0441.01080.0FaYSBrkr1656016561.00.01031TA7Typ2GdAttchd1960.0Fin2.0528.0TATAP21062NaNNaN215000
152635004020RH11622PaveRegLvlAllPubInsideNAmesFeedr1Fam1Story5619611961GableVinylSdVinylSdTATACBlockTATANoRec468.0144.0270.0882.0TAYSBrkr89608960.00.01021TA5Typ0NaNAttchd1961.0Unf1.0730.0TATAY1400NaNMnPrv105000
252635101020RL14267PaveIR1LvlAllPubCornerNAmesNorm1Fam1Story6619581958HipWd SdngWd SdngTATACBlockTATANoALQ923.00.0406.01329.0TAYSBrkr1329013290.00.01131Gd6Typ0NaNAttchd1958.0Unf1.0312.0TATAY39336NaNNaN172000
352635303020RL11160PaveRegLvlAllPubCornerNAmesNorm1Fam1Story7519681968HipBrkFaceBrkFaceGdTACBlockTATANoALQ1065.00.01045.02110.0ExYSBrkr2110021101.00.02131Ex8Typ2TAAttchd1968.0Fin2.0522.0TATAY00NaNNaN244000
452710501060RL13830PaveIR1LvlAllPubInsideGilbertNorm1Fam2Story5519971998GableVinylSdVinylSdTATAPConcGdTANoGLQ791.00.0137.0928.0GdYSBrkr92870116290.00.02131TA6Typ1TAAttchd1997.0Fin2.0482.0TATAY21234NaNMnPrv189900
552710503060RL9978PaveIR1LvlAllPubInsideGilbertNorm1Fam2Story6619981998GableVinylSdVinylSdTATAPConcTATANoGLQ602.00.0324.0926.0ExYSBrkr92667816040.00.02131Gd7Typ1GdAttchd1998.0Fin2.0470.0TATAY36036NaNNaN195500
6527127150120RL4920PaveRegLvlAllPubInsideStoneBrNormTwnhsE1Story8520012001GableCemntBdCmentBdGdTAPConcGdTAMnGLQ616.00.0722.01338.0ExYSBrkr1338013381.00.02021Gd6Typ0NaNAttchd2001.0Fin2.0582.0TATAY00NaNNaN213500
7527145080120RL5005PaveIR1HLSAllPubInsideStoneBrNormTwnhsE1Story8519921992GableHdBoardHdBoardGdTAPConcGdTANoALQ263.00.01017.01280.0ExYSBrkr1280012800.00.02021Gd5Typ0NaNAttchd1992.0RFn2.0506.0TATAY082NaNNaN191500
8527146030120RL5389PaveIR1LvlAllPubInsideStoneBrNormTwnhsE1Story8519951996GableCemntBdCmentBdGdTAPConcGdTANoGLQ1180.00.0415.01595.0ExYSBrkr1616016161.00.02021Gd5Typ1TAAttchd1995.0RFn2.0608.0TATAY237152NaNNaN236500
952716213060RL7500PaveRegLvlAllPubInsideGilbertNorm1Fam2Story7519991999GableVinylSdVinylSdTATAPConcTATANoUnf0.00.0994.0994.0GdYSBrkr102877618040.00.02131Gd7Typ1TAAttchd1999.0Fin2.0442.0TATAY14060NaNNaN189000
pidms_subclassms_zoninglot_areastreetlot_shapeland_contourutilitieslot_configneighborhoodcondition_1bldg_typehouse_styleoverall_qualoverall_condyear_builtyear_remod_addroof_styleexterior_1stexterior_2ndexter_qualexter_condfoundationbsmt_qualbsmt_condbsmt_exposurebsmtfin_type_1bsmtfin_sf_1bsmtfin_sf_2bsmt_unf_sftotal_bsmt_sfheating_qccentral_airelectrical1st_flr_sf2nd_flr_sfgr_liv_areabsmt_full_bathbsmt_half_bathfull_bathhalf_bathbedroom_abvgrkitchen_abvgrkitchen_qualtotrms_abvgrdfunctionalfireplacesfireplace_qugarage_typegarage_yr_bltgarage_finishgarage_carsgarage_areagarage_qualgarage_condpaved_drivewood_deck_sfopen_porch_sfpool_qcfencesaleprice
2920923228310160RM1894PaveRegLvlAllPubInsideMeadowVNormTwnhsE2Story4519701970GableCemntBdCmentBdTATACBlockTATANoRec252.00.0294.0546.0TAYSBrkr54654610920.00.01131TA6Typ0NaNCarPort1970.0Unf1.0286.0TATAY024NaNNaN71000
292192322911090RL12640PaveIR1LvlAllPubInsideMitchelNormDuplex1Story6519761976GablePlywoodPlywoodTATACBlockTATAGdRec936.0396.0396.01728.0TAYSBrkr1728017280.00.02042TA8Typ0NaNAttchd1976.0Unf2.0574.0TATAY400NaNNaN150900
292292323004090RL9297PaveRegLvlAllPubInsideMitchelNormDuplex1Story5519761976GablePlywoodPlywoodTATACBlockTATANoALQ1606.00.0122.01728.0TAYSBrkr1728017282.00.02042TA8Typ0NaNDetchd1976.0Unf2.0560.0TATAY00NaNNaN188000
292392325006020RL17400PaveRegLowAllPubInsideMitchelNorm1Fam1Story5519771977GableBrkFaceBrkFaceTATACBlockTATANoALQ936.00.0190.01126.0FaYSBrkr1126011261.00.02031TA5Typ1GdAttchd1977.0RFn2.0484.0TATAP29541NaNNaN160000
292492325118020RL20000PaveRegLvlAllPubInsideMitchelNorm1Fam1Story5719601996GableVinylSdVinylSdTATACBlockTATANoALQ1224.00.00.01224.0ExYSBrkr1224012241.00.01041TA7Typ1TADetchd1960.0Unf2.0576.0TATAY4740NaNNaN131000
292592327508080RL7937PaveIR1LvlAllPubCulDSacMitchelNorm1FamSLvl6619841984GableHdBoardHdBoardTATACBlockTATAAvGLQ819.00.0184.01003.0TAYSBrkr1003010031.00.01031TA6Typ0NaNDetchd1984.0Unf2.0588.0TATAY1200NaNGdPrv142500
292692327610020RL8885PaveIR1LowAllPubInsideMitchelNorm1Fam1Story5519831983GableHdBoardHdBoardTATACBlockGdTAAvBLQ301.0324.0239.0864.0TAYSBrkr90209021.00.01021TA5Typ0NaNAttchd1983.0Unf2.0484.0TATAY1640NaNMnPrv131000
292792340012585RL10441PaveRegLvlAllPubInsideMitchelNorm1FamSFoyer5519921992GableHdBoardWd ShngTATAPConcGdTAAvGLQ337.00.0575.0912.0TAYSBrkr97009700.01.01031TA6Typ0NaNNaNNaNNaN0.00.0NaNNaNY8032NaNMnPrv132000
292892410007020RL10010PaveRegLvlAllPubInsideMitchelNorm1Fam1Story5519741975GableHdBoardHdBoardTATACBlockGdTAAvALQ1071.0123.0195.01389.0GdYSBrkr1389013891.00.01021TA6Typ1TAAttchd1975.0RFn2.0418.0TATAY24038NaNNaN170000
292992415105060RL9627PaveRegLvlAllPubInsideMitchelNorm1Fam2Story7519931994GableHdBoardHdBoardTATAPConcGdTAAvLwQ758.00.0238.0996.0ExYSBrkr996100420000.00.02131TA9Typ1TAAttchd1993.0Fin3.0650.0TATAY19048NaNNaN188000